{"id":"W3159065547","doi":"","title":"Overview of TAC-KBP 2019 Fine-grained Entity Extraction.","year":2019,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Extraction (chemistry); Chemistry; Chromatography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003250993,0.00007780931,0.0002316133,0.000117637,0.00007368796,0.00004522043,0.0004447888,0.00003584566,0.0009008383],"category_scores_gemma":[0.000260826,0.00006035039,0.00005931501,0.0004168828,0.0002389787,0.0003566047,0.0001628586,0.00005142266,0.0001966021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004423508,"about_ca_system_score_gemma":0.00002664658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004480304,"about_ca_topic_score_gemma":0.00001417432,"domain_scores_codex":[0.9987112,0.0001685075,0.0004519033,0.0002295134,0.0003510732,0.00008774659],"domain_scores_gemma":[0.9976933,0.001030121,0.0003108121,0.0007524455,0.0001786663,0.00003462498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003747055,0.00006945065,0.0003592769,0.00005520059,0.00001617558,3.977247e-8,0.000177754,0.00000792927,0.0008097318,0.9594868,0.002405533,0.03657466],"study_design_scores_gemma":[0.0001524264,0.00003470973,0.003826041,0.000007232062,0.00002183237,7.785409e-7,0.001342101,0.000007874904,0.003192965,0.8016,0.1897442,0.00006980781],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5743743,0.007097547,0.3209994,0.002406447,0.0004787115,0.002554606,0.0008612665,0.0001155841,0.09111226],"genre_scores_gemma":[0.9879637,0.000289924,0.0003483643,0.00006747119,0.00002513402,0.00003896214,0.00003732599,0.000004030952,0.0112251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4135894,"threshold_uncertainty_score":0.9863548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06709713054902026,"score_gpt":0.3938583595584118,"score_spread":0.3267612290093915,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}