Card: experiment-01 LLM label columns for eval_sample

README.md

@@ -217,9 +217,11 @@ Splits are by document (80/10/10 on a hash of `document_id`), shared by both con
 |---|---|
 | `excerpt_id`, `document_id`, `type`, `section_category`, `page` | Identity and position |
 | `text` | Verbatim excerpt; median 34 words, p90 107 |
-| `themes` (22 codes), `regions` (17), `countries` (198) | Findings and recommendations |
-| `methods` (24 codes) | Methodology excerpts |
+| `themes` (22 codes), `regions` (17), `countries` (198) | Pipeline labels, findings and recommendations. Tagged with the whole section as input |
+| `methods` (24 codes) | Pipeline labels, methodology excerpts |
 | `eval_sample` | `true` for the fixed experiment-01 test sample: 300 findings, 150 recommendations, 150 methodology, seed 0 |
+| `<field>_glm`, `<field>_deepseek`, `<field>_qwen` | `eval_sample` rows only: labels from GLM-5.3-Flash, DeepSeek-V4.1-Flash and Qwen3.8-Flash-Next, given the excerpt alone, with classify-only prompts rebuilt from the pipeline's ([prompts](https://github.com/baobab-tech/decision-models-experiments/blob/main/experiments/common/prompts/excerpt-tagging.md)). Generated 2026-10-03 |
+| `<field>_majority` | `eval_sample` rows only: labels chosen by at least 2 of the 3 LLMs. The experiment-01 reference |
 
 ### `taxonomy`
 
@@ -246,4 +248,4 @@ Earlier Baobab Tech classifier runs on `documents` test (134), mean field score
 
 ## Licence
 
-The reports are publicly published by their organisations, which keep their rights; check each report's terms before reusing its text beyond research. Labels, splits and taxonomy are released by Baobab Tech.
+The reports are publicly published by their organisations, which keep their rights; check each report's terms before reusing its text beyond research. Labels, splits and taxonomy are released by Baobab Tech. No labels in this dataset are human gold labels except the 36 hand-corrected documents (`label_source_pipeline == "manual"`); all others are LLM output.