Whether a gold-standard annotation for a given attribute exists on a document. In the comparison CSV this appears as attribute_presence ("True" or "False"). Absence does not always mean the gold value used for scoring is empty: missing annotations may still be represented with a type-specific default when metrics are computed.
Page number(s) parsed from EPPI citation markup in gold full-text details (comparison CSV column citation_page). Empty when markup is missing or not EPPI-sourced.
A 0–100 score for how well a support snippet is grounded in the document text used for extraction. For gold rows, the snippet is human_additional_text (the human annotator's supporting / verbatim text on the gold annotation). For LLM rows, the snippet is llm_verbatim_text (the model's additional_text). Scores are written to human_verbatim_fuzzy_match_pct and llm_verbatim_fuzzy_match_pct in the comparison CSV. Empty snippets score 0.
For STRING / INTEGER / FLOAT evaluation, a scored document-attribute instance where the gold value is found in parsed context (the text the LLM read). Counted in n_good_source_instances.
For STRING / INTEGER / FLOAT evaluation, whether the gold value is found in citation text (additional_text and/or item_attribute_full_text_details). Exported per row as gold_value_in_citation in goldstandard_llm_comparison.csv.
Row-level outcome label in goldstandard_llm_comparison.csv for STRING / INTEGER / FLOAT (match_status column). Values: exact_match (gold equals LLM), near_match (STRING only: normalised Levenshtein similarity at or above the edit-distance threshold), missing_prediction (no LLM value), extraction_error_bad_source (mismatch and gold not in parsed context), extraction_error_good_source (mismatch and gold is in parsed context). Empty for BOOL rows.