[ { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q1", "question": "What are the main analytes type studied?", "choices": { "A": "Soft tissue", "B": "Oral disease", "C": "Todd Hewitt Broth", "D": "Periodontal pathogens", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Analytes", "term_explanation": "The specific substances being studied in the context of SERS analysis, such as biomolecules (proteins, DNA, RNA), chemical compounds (pharmaceuticals, pollutants), or biological samples (cells, bacteria). The choice of analyte impacts the Raman signal, and it’s essential to document the concentration and physical state of the analyte (e.g., liquid, solid, powder)." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q2", "question": "What are the material and structure, or morphology of the SERS substrates used?", "choices": { "A": "Gold", "B": "Sliver", "C": "Titanium layer", "D": "AFM", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "SERS Substrates", "term_explanation": "The types and structural properties of the SERS substrates used, including material (e.g., silver, gold), size, and morphology (e.g., nanospheres, nanostars, chiral metamaterials). Substrate shape and roughness influence plasmonic properties, affecting the enhancement factor." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q3", "question": "How many analytes are investigated?", "choices": { "A": "2", "B": "3", "C": "4", "D": "5", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Number of Analytes", "term_explanation": "The total number of analytes included in the study. This helps assess the breadth of the study and the potential for generalization of the findings across different analyte types." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q4", "question": "What is the excitation laser wavelength used for SERS measurements?", "choices": { "A": "90 mW", "B": "633 nm", "C": "785 nm", "D": "30 s", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Laser wavelength", "term_explanation": "The specific wavelength of the laser used for excitation in the Raman setup, as different wavelengths may resonate more strongly with different analytes or substrates, influencing the SERS enhancement factor." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q5", "question": "What is the spectral range collected for the analysis of the analytes?", "choices": { "A": "301 cm⁻¹ to 2000 cm⁻¹", "B": "300 cm⁻¹ to 2001 cm⁻¹", "C": "400 cm⁻¹ to 4000 cm⁻¹", "D": "200 cm⁻¹ to 2000 cm⁻¹", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Spectral Range", "term_explanation": "The range of wavelengths or wavenumbers collected during the experiment (e.g., 400–2000 cm⁻¹). Different ranges target different molecular vibrations, so this impacts the chemical information obtained." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q6", "question": "How many spectra are collected per analyte under each experimental condition?", "choices": { "A": "100", "B": "80", "C": "10", "D": "35", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Number of Spectra for Each Analyte Under Each Condition", "term_explanation": "The total number of spectra collected for each analyte, condition, or experimental setup, which gives insight into the dataset’s size and statistical robustness." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q7", "question": "What is the primary machine learning task addressed in this study?", "choices": { "A": "Regression", "B": "Clustering", "C": "Dimension reduction", "D": "Classification", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning task", "term_explanation": "A Machine Learning task is a formalized problem or objective that specifies the type of patterns or predictions a model needs to learn from data, including the nature of the input, output, and how they are related. (Classification, regression, clustering etc.)" } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q8", "question": "Which machine learning algorithm is implemented?", "choices": { "A": "PCA", "B": "SVM", "C": "KNN", "D": "RF", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning algorithm", "term_explanation": "A Machine Learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q9", "question": "What data splitting strategy is applied, and the parameters?", "choices": { "A": "T-T split; 0.7, 0.3", "B": "T-D-T split; 0.525, 0.175, 0.3", "C": "K-fold; 7 fold", "D": "K-fold; 10 fold", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Splitting strategy (tdt split or k-fold)", "term_explanation": "The process of training and validating the AI models, including strategies like train-test splitting or k-fold cross-validation, the number of training epochs, and how training performance is tracked (e.g., loss curves)." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q10", "question": "How many experimental replications are conducted to ensure reproducibility?", "choices": { "A": "5", "B": "256", "C": "1000", "D": "35", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Number of Replications", "term_explanation": "The number of times the experiment is repeated to ensure reproducibility and accuracy." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q11", "question": "How many epochs are used during model training?", "choices": { "A": "5", "B": "256", "C": "1000", "D": "35", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Epochs", "term_explanation": "The total number of complete passes through the entire dataset during training for machine learning models." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q12", "question": "What performance metrics are employed to evaluate the machine learning models?", "choices": { "A": "Accuracy", "B": "Precision", "C": "Recall", "D": "F1 score", "E": "All of above", "F": "None of above" }, "answer": "E", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance metrics", "term_explanation": "The metrics used to evaluate model performance, including accuracy, precision, recall, F1 score, and AUC, providing insight into the model's effectiveness." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "1", "paper_title": "Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy", "question_id": "Q13", "question": "What are the reported performance values?", "choices": { "A": "Detection accuracy of 99.7 % ± 0.5 % for Aa, 99.2 % ± 0.7 % for Pg, and 97.8 % ± 0.9 % for Sm", "B": ">97.8 % accuracy, >98.2 % precision, >98.2 % recall and >98.2 % F1 score", "C": "p < 0.003 for any classical method compared to our method", "D": "89.5±2.5 % for Aa, 86.2±2.2 % for Pg, 87.4±3.1 % for Sm.", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance values", "term_explanation": "The performance value finally reported" } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q1", "question": "What are the main analytes type studied?", "choices": { "A": "AGP", "B": "Protein", "C": "Blood plasma", "D": "Glycosylate", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Analytes", "term_explanation": "The specific substances being studied in the context of SERS analysis, such as biomolecules (proteins, DNA, RNA), chemical compounds (pharmaceuticals, pollutants), or biological samples (cells, bacteria). The choice of analyte impacts the Raman signal, and it’s essential to document the concentration and physical state of the analyte (e.g., liquid, solid, powder)." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q2", "question": "What are the material and structure, or morphology of the SERS substrates used?", "choices": { "A": "Gold", "B": "Sliver", "C": "Surface plasmon polaritons", "D": "AFM", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "SERS Substrates", "term_explanation": "The types and structural properties of the SERS substrates used, including material (e.g., silver, gold), size, and morphology (e.g., nanospheres, nanostars, chiral metamaterials). Substrate shape and roughness influence plasmonic properties, affecting the enhancement factor." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q3", "question": "How many analytes are investigated?", "choices": { "A": "4", "B": "3", "C": "2", "D": "1", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Number of Analytes", "term_explanation": "The total number of analytes included in the study. This helps assess the breadth of the study and the potential for generalization of the findings across different analyte types." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q4", "question": "What is the excitation laser wavelength used for SERS measurements?", "choices": { "A": "633 nm", "B": "532 nm", "C": "785 nm", "D": "514 nm", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Laser wavelength", "term_explanation": "The specific wavelength of the laser used for excitation in the Raman setup, as different wavelengths may resonate more strongly with different analytes or substrates, influencing the SERS enhancement factor." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q5", "question": "What is the spectral range collected for the analysis of the analytes?", "choices": { "A": "300 cm⁻¹ to 2000 cm⁻¹", "B": "300 cm⁻¹ to 3000 cm⁻¹", "C": "400 cm⁻¹ to 3000 cm⁻¹", "D": "400 cm⁻¹ to 4000 cm⁻¹", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Spectral Range", "term_explanation": "The range of wavelengths or wavenumbers collected during the experiment (e.g., 400–2000 cm⁻¹). Different ranges target different molecular vibrations, so this impacts the chemical information obtained." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q6", "question": "How many spectra are collected per analyte under each experimental condition?", "choices": { "A": "10", "B": "80", "C": "100", "D": "35", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Number of Spectra for Each Analyte Under Each Condition", "term_explanation": "The total number of spectra collected for each analyte, condition, or experimental setup, which gives insight into the dataset’s size and statistical robustness." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q7", "question": "What is the primary machine learning task addressed in this study?", "choices": { "A": "Regression", "B": "Clustering", "C": "Dimension reduction", "D": "Classification", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning task", "term_explanation": "A Machine Learning task is a formalized problem or objective that specifies the type of patterns or predictions a model needs to learn from data, including the nature of the input, output, and how they are related. (Classification, regression, clustering etc.)" } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q8", "question": "Which machine learning algorithm is implemented?", "choices": { "A": "ANN", "B": "CNN", "C": "DNN", "D": "RNN", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning algorithm", "term_explanation": "A Machine Learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q9", "question": "What data splitting strategy is applied, and the parameters?", "choices": { "A": "T-T split; 0.7, 0.3", "B": "T-D-T split; 0.8, 0.1, 0.1", "C": "K-fold; 7 fold", "D": "K-fold; 10 fold", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Splitting strategy (tdt split or k-fold)", "term_explanation": "The process of training and validating the AI models, including strategies like train-test splitting or k-fold cross-validation, the number of training epochs, and how training performance is tracked (e.g., loss curves)." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q10", "question": "How many experimental replications are conducted to ensure reproducibility?", "choices": { "A": "371", "B": "1733", "C": "195", "D": "487", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Number of Replications", "term_explanation": "The number of times the experiment is repeated to ensure reproducibility and accuracy." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q11", "question": "How many epochs are used during model training?", "choices": { "A": "50", "B": "300", "C": "1000", "D": "3500", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Epochs", "term_explanation": "The total number of complete passes through the entire dataset during training for machine learning models." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q12", "question": "What performance metrics are employed to evaluate the machine learning models?", "choices": { "A": "Accuracy", "B": "Precision", "C": "Recall", "D": "Predicted vs. Real Concentrations", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance metrics", "term_explanation": "The metrics used to evaluate model performance, including accuracy, precision, recall, F1 score, and AUC, providing insight into the model's effectiveness." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "2", "paper_title": "Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach", "question_id": "Q13", "question": "What are the reported performance values?", "choices": { "A": "Detection accuracy of 99.7 %", "B": "97.8 % accuracy, >98.2 % precision, >98.2 % recall and >98.2 % F1 score", "C": "p < 0.003", "D": "A strong correlation", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance values", "term_explanation": "The performance value finally reported" } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q1", "question": "What are the main analytes type studied?", "choices": { "A": "Chemical Detection", "B": "2-dimensional physically activated chemical", "C": "R800", "D": "Single molecule", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Analytes", "term_explanation": "The specific substances being studied in the context of SERS analysis, such as biomolecules (proteins, DNA, RNA), chemical compounds (pharmaceuticals, pollutants), or biological samples (cells, bacteria). The choice of analyte impacts the Raman signal, and it’s essential to document the concentration and physical state of the analyte (e.g., liquid, solid, powder)." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q2", "question": "What are the material and structure, or morphology of the SERS substrates used?", "choices": { "A": "Gold rod", "B": "Sliver nanoparticles", "C": "Gold nanoparticles", "D": "Two-dimensional physically activated chemical", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "SERS Substrates", "term_explanation": "The types and structural properties of the SERS substrates used, including material (e.g., silver, gold), size, and morphology (e.g., nanospheres, nanostars, chiral metamaterials). Substrate shape and roughness influence plasmonic properties, affecting the enhancement factor." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q3", "question": "How many analytes are investigated?", "choices": { "A": "4", "B": "3", "C": "2", "D": "1", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Number of Analytes", "term_explanation": "The total number of analytes included in the study. This helps assess the breadth of the study and the potential for generalization of the findings across different analyte types." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q4", "question": "What is the excitation laser wavelength used for SERS measurements?", "choices": { "A": "633 nm", "B": "532 nm", "C": "785 nm", "D": "514 nm", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Laser wavelength", "term_explanation": "The specific wavelength of the laser used for excitation in the Raman setup, as different wavelengths may resonate more strongly with different analytes or substrates, influencing the SERS enhancement factor." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q5", "question": "What is the spectral range collected for the analysis of the analytes?", "choices": { "A": "300 cm⁻¹ to 2000 cm⁻¹", "B": "300 cm⁻¹ to 3000 cm⁻¹", "C": "400 cm⁻¹ to 3000 cm⁻¹", "D": "400 cm⁻¹ to 4000 cm⁻¹", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Spectral Range", "term_explanation": "The range of wavelengths or wavenumbers collected during the experiment (e.g., 400–2000 cm⁻¹). Different ranges target different molecular vibrations, so this impacts the chemical information obtained." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q6", "question": "How many spectra are collected per analyte under each experimental condition?", "choices": { "A": "2940", "B": "64", "C": "800", "D": "50", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Number of Spectra for Each Analyte Under Each Condition", "term_explanation": "The total number of spectra collected for each analyte, condition, or experimental setup, which gives insight into the dataset’s size and statistical robustness." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q7", "question": "What is the primary machine learning task addressed in this study?", "choices": { "A": "Regression", "B": "Clustering", "C": "Dimension reduction", "D": "Classification", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning task", "term_explanation": "A Machine Learning task is a formalized problem or objective that specifies the type of patterns or predictions a model needs to learn from data, including the nature of the input, output, and how they are related. (Classification, regression, clustering etc.)" } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q8", "question": "Which machine learning algorithm is implemented?", "choices": { "A": "ANN", "B": "CNN", "C": "DNN", "D": "RNN", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning algorithm", "term_explanation": "A Machine Learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q9", "question": "What data splitting strategy is applied, and the parameters?", "choices": { "A": "T-T split; 0.7, 0.3", "B": "T-D-T split; 0.8, 0.1, 0.1", "C": "K-fold; 5 fold", "D": "K-fold; 10 fold", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Splitting strategy (tdt split or k-fold)", "term_explanation": "The process of training and validating the AI models, including strategies like train-test splitting or k-fold cross-validation, the number of training epochs, and how training performance is tracked (e.g., loss curves)." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q10", "question": "How many experimental replications are conducted to ensure reproducibility?", "choices": { "A": "5", "B": "256", "C": "1000", "D": "35", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Number of Replications", "term_explanation": "The number of times the experiment is repeated to ensure reproducibility and accuracy." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q11", "question": "How many epochs are used during model training?", "choices": { "A": "50", "B": "300", "C": "1000", "D": "3500", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Epochs", "term_explanation": "The total number of complete passes through the entire dataset during training for machine learning models." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q12", "question": "What performance metrics are employed to evaluate the machine learning models?", "choices": { "A": "MSE", "B": "R squre", "C": "LOB", "D": "Predicted vs. Real Concentrations", "E": "All of above", "F": "None of above" }, "answer": "E", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance metrics", "term_explanation": "The metrics used to evaluate model performance, including accuracy, precision, recall, F1 score, and AUC, providing insight into the model's effectiveness." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "3", "paper_title": "Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks", "question_id": "Q13", "question": "What are the reported performance values?", "choices": { "A": "MSE, 0.111", "B": "R squre, 0.958", "C": "LOB, 1 fM", "D": "LOQ, 10 fM", "E": "All of above", "F": "None of above" }, "answer": "E", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance values", "term_explanation": "The performance value finally reported" } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q1", "question": "What are the main analytes type studied?", "choices": { "A": "FASS", "B": "buffer", "C": "CoV NL63", "D": "SARS-Cov-2", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Analytes", "term_explanation": "The specific substances being studied in the context of SERS analysis, such as biomolecules (proteins, DNA, RNA), chemical compounds (pharmaceuticals, pollutants), or biological samples (cells, bacteria). The choice of analyte impacts the Raman signal, and it’s essential to document the concentration and physical state of the analyte (e.g., liquid, solid, powder)." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q2", "question": "What are the material and structure, or morphology of the SERS substrates used?", "choices": { "A": "Gold rod", "B": "Sliver nanoparticles", "C": "Gold nanoparticles", "D": "Two-dimensional physically activated chemical", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "SERS Substrates", "term_explanation": "The types and structural properties of the SERS substrates used, including material (e.g., silver, gold), size, and morphology (e.g., nanospheres, nanostars, chiral metamaterials). Substrate shape and roughness influence plasmonic properties, affecting the enhancement factor." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q3", "question": "How many analytes are investigated?", "choices": { "A": "4", "B": "3", "C": "2", "D": "1", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Number of Analytes", "term_explanation": "The total number of analytes included in the study. This helps assess the breadth of the study and the potential for generalization of the findings across different analyte types." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q4", "question": "What is the excitation laser wavelength used for SERS measurements?", "choices": { "A": "633 nm", "B": "532 nm", "C": "785 nm", "D": "514 nm", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Laser wavelength", "term_explanation": "The specific wavelength of the laser used for excitation in the Raman setup, as different wavelengths may resonate more strongly with different analytes or substrates, influencing the SERS enhancement factor." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q5", "question": "What is the spectral range collected for the analysis of the analytes?", "choices": { "A": "600 cm⁻¹ to 1800 cm⁻¹", "B": "600 cm⁻¹ to 850 cm⁻¹", "C": "912 cm⁻¹ to 1157 cm⁻¹", "D": "600 cm⁻¹ to 1664 cm⁻¹", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Spectral Range", "term_explanation": "The range of wavelengths or wavenumbers collected during the experiment (e.g., 400–2000 cm⁻¹). Different ranges target different molecular vibrations, so this impacts the chemical information obtained." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q6", "question": "How many spectra are collected per analyte under each experimental condition?", "choices": { "A": "60", "B": "20", "C": "60 and 20", "D": "2512 and 2360", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Number of Spectra for Each Analyte Under Each Condition", "term_explanation": "The total number of spectra collected for each analyte, condition, or experimental setup, which gives insight into the dataset’s size and statistical robustness." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q7", "question": "What is the primary machine learning task addressed in this study?", "choices": { "A": "Regression", "B": "Clustering", "C": "Dimension reduction", "D": "Classification", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning task", "term_explanation": "A Machine Learning task is a formalized problem or objective that specifies the type of patterns or predictions a model needs to learn from data, including the nature of the input, output, and how they are related. (Classification, regression, clustering etc.)" } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q8", "question": "Which machine learning algorithm is implemented?", "choices": { "A": "SVM", "B": "CNN", "C": "LR", "D": "RNN", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning algorithm", "term_explanation": "A Machine Learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q9", "question": "What data splitting strategy is applied, and the parameters?", "choices": { "A": "T-T split; 0.7, 0.3", "B": "T-D-T split; 0.8, 0.1, 0.1", "C": "K-fold; 5 fold", "D": "K-fold; 10 fold", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Splitting strategy (tdt split or k-fold)", "term_explanation": "The process of training and validating the AI models, including strategies like train-test splitting or k-fold cross-validation, the number of training epochs, and how training performance is tracked (e.g., loss curves)." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q10", "question": "How many experimental replications are conducted to ensure reproducibility?", "choices": { "A": "5", "B": "256", "C": "1000", "D": "35", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Number of Replications", "term_explanation": "The number of times the experiment is repeated to ensure reproducibility and accuracy." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q11", "question": "How many epochs are used during model training?", "choices": { "A": "50", "B": "170", "C": "600", "D": "1700", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Epochs", "term_explanation": "The total number of complete passes through the entire dataset during training for machine learning models." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q12", "question": "What performance metrics are employed to evaluate the machine learning models?", "choices": { "A": "Accuracy", "B": "Precision", "C": "Recall", "D": "Predicted vs. Real Concentrations", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance metrics", "term_explanation": "The metrics used to evaluate model performance, including accuracy, precision, recall, F1 score, and AUC, providing insight into the model's effectiveness." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "4", "paper_title": "Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms", "question_id": "Q13", "question": "What are the reported performance values?", "choices": { "A": "0.993", "B": "0.989", "C": "0.977", "D": "1.000", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance values", "term_explanation": "The performance value finally reported" } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q1", "question": "What are the main analytes type studied?", "choices": { "A": "ACE2", "B": "SARS-Cov-2 B1", "C": "CoV NL63", "D": "SARS-Cov-2", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Analytes", "term_explanation": "The specific substances being studied in the context of SERS analysis, such as biomolecules (proteins, DNA, RNA), chemical compounds (pharmaceuticals, pollutants), or biological samples (cells, bacteria). The choice of analyte impacts the Raman signal, and it’s essential to document the concentration and physical state of the analyte (e.g., liquid, solid, powder)." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q2", "question": "What are the material and structure, or morphology of the SERS substrates used?", "choices": { "A": "Gold rod", "B": "Sliver nanoparticles", "C": "Gold nanoparticles", "D": "Two-dimensional physically activated chemical", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "SERS Substrates", "term_explanation": "The types and structural properties of the SERS substrates used, including material (e.g., silver, gold), size, and morphology (e.g., nanospheres, nanostars, chiral metamaterials). Substrate shape and roughness influence plasmonic properties, affecting the enhancement factor." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q3", "question": "How many analytes are investigated?", "choices": { "A": "4", "B": "3", "C": "2", "D": "1", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Study subject & experimental setup", "question_key_term": "Number of Analytes", "term_explanation": "The total number of analytes included in the study. This helps assess the breadth of the study and the potential for generalization of the findings across different analyte types." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q4", "question": "What is the excitation laser wavelength used for SERS measurements?", "choices": { "A": "633 nm", "B": "532 nm", "C": "785 nm", "D": "514 nm", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Laser wavelength", "term_explanation": "The specific wavelength of the laser used for excitation in the Raman setup, as different wavelengths may resonate more strongly with different analytes or substrates, influencing the SERS enhancement factor." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q5", "question": "What is the spectral range collected for the analysis of the analytes?", "choices": { "A": "600 cm⁻¹ to 1800 cm⁻¹", "B": "400 cm⁻¹ to 1850 cm⁻¹", "C": "400 cm⁻¹ to 1800 cm⁻¹", "D": "600 cm⁻¹ to 1850 cm⁻¹", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Spectral Range", "term_explanation": "The range of wavelengths or wavenumbers collected during the experiment (e.g., 400–2000 cm⁻¹). Different ranges target different molecular vibrations, so this impacts the chemical information obtained." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q6", "question": "How many spectra are collected per analyte under each experimental condition?", "choices": { "A": "Less than 200", "B": "200", "C": "More than 200", "D": "1200", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Number of Spectra for Each Analyte Under Each Condition", "term_explanation": "The total number of spectra collected for each analyte, condition, or experimental setup, which gives insight into the dataset’s size and statistical robustness." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q7", "question": "What is the primary machine learning task addressed in this study?", "choices": { "A": "Regression", "B": "Classification", "C": "Dimension reduction", "D": "Classification and regression", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning task", "term_explanation": "A Machine Learning task is a formalized problem or objective that specifies the type of patterns or predictions a model needs to learn from data, including the nature of the input, output, and how they are related. (Classification, regression, clustering etc.)" } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q8", "question": "Which machine learning algorithm is implemented?", "choices": { "A": "ANN", "B": "CNN", "C": "DNN", "D": "RNN", "E": "All of above", "F": "None of above" }, "answer": "B", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Machine Learning algorithm", "term_explanation": "A Machine Learning algorithm (such as linear regression, support vector machine, DNN etc. ) is a mathematical framework or procedure that processes input data to create a model capable of solving a specific ML task." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q9", "question": "What data splitting strategy is applied, and the parameters?", "choices": { "A": "T-T split; 0.7, 0.3", "B": "T-D-T split; 0.8, 0.1, 0.1", "C": "K-fold; 5 fold", "D": "K-fold; 10 fold", "E": "All of above", "F": "None of above" }, "answer": "D", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Splitting strategy (tdt split or k-fold)", "term_explanation": "The process of training and validating the AI models, including strategies like train-test splitting or k-fold cross-validation, the number of training epochs, and how training performance is tracked (e.g., loss curves)." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q10", "question": "How many experimental replications are conducted to ensure reproducibility?", "choices": { "A": "10", "B": "256", "C": "1000", "D": "35", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Data characteristics & collection", "question_key_term": "Number of Replications", "term_explanation": "The number of times the experiment is repeated to ensure reproducibility and accuracy." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q11", "question": "How many epochs are used during model training?", "choices": { "A": "100", "B": "200", "C": "300", "D": "4700", "E": "All of above", "F": "None of above" }, "answer": "C", "metadata": { "Task-oriented Category": "Technical approach & details", "question_key_term": "Number of Epochs", "term_explanation": "The total number of complete passes through the entire dataset during training for machine learning models." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q12", "question": "What performance metrics are employed to evaluate the machine learning models?", "choices": { "A": "Accuracy", "B": "R squre", "C": "Recall", "D": "MAE", "E": "All of above", "F": "None of above" }, "answer": "F", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance metrics", "term_explanation": "The metrics used to evaluate model performance, including accuracy, precision, recall, F1 score, and AUC, providing insight into the model's effectiveness." } }, { "subject": "Physics - Surface Enhanced Raman Spectroscopy", "paper_id": "5", "paper_title": "Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms", "question_id": "Q13", "question": "What are the reported performance values?", "choices": { "A": "Acc, 0.999; R square 0.98", "B": "Acc, 0.999", "C": "R square 0.98", "D": "R square 0.993", "E": "All of above", "F": "None of above" }, "answer": "A", "metadata": { "Task-oriented Category": "Conclusions & results", "question_key_term": "Performance values", "term_explanation": "The performance value finally reported" } } ]