{"id":"W3158985534","doi":"","title":"GAIA at SM-KBP 2019 - A Multi-media Multi-lingual Knowledge Extraction and Hypothesis Generation System.","year":2019,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Extraction (chemistry); Natural language processing; Artificial intelligence; 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":[0.0004912931,0.0001107754,0.0001559961,0.00007970876,0.0001661695,0.0000542326,0.0001992527,0.000070704,0.000006203482],"category_scores_gemma":[0.00003215864,0.0001022792,0.00002454486,0.0001166625,0.00008827809,0.0002720653,0.0001215992,0.00006376652,0.00004233022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002798356,"about_ca_system_score_gemma":0.00003316647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001911534,"about_ca_topic_score_gemma":0.00001849193,"domain_scores_codex":[0.9991804,0.00009144253,0.0002273814,0.0003111348,0.00007317449,0.000116445],"domain_scores_gemma":[0.9990032,0.0003094952,0.0001237822,0.0004198791,0.00009242026,0.00005117383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001819323,0.00008741471,0.0005114233,0.0001466752,0.00002141709,1.851807e-7,0.004239154,0.0001786856,0.03562902,0.8720799,0.00003219221,0.08705576],"study_design_scores_gemma":[0.004878211,0.00021894,0.02354354,0.0002109187,0.0002599702,0.0002193171,0.008484994,0.6145249,0.2696543,0.06380066,0.01223508,0.001969137],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3681444,0.001453735,0.6295809,0.00003618225,0.0001195502,0.0003173429,0.000006827891,0.00007456206,0.0002665451],"genre_scores_gemma":[0.9740162,0.00008188793,0.02463045,0.00001157815,0.0001007238,0.00008084327,0.000008360907,0.000008235425,0.001061698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8082792,"threshold_uncertainty_score":0.4170825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02806175378511928,"score_gpt":0.2686272007403758,"score_spread":0.2405654469552566,"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."}}