{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004455001,0.002129157,0.001629117,0.005007918,0.001441043,0.003364484,0.003225037,0.002757506,0.03547538],"category_scores_gemma":[0.01786955,0.001123101,0.001597309,0.002341156,0.0007420459,0.005948069,0.004255306,0.002601101,0.03401197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008144039,"about_ca_system_score_gemma":0.002331938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005095944,"about_ca_topic_score_gemma":0.006427469,"domain_scores_codex":[0.9969823,0.00130304,0.0002223207,0.000791874,0.0005646166,0.0001357704],"domain_scores_gemma":[0.99319,0.003858156,0.000232596,0.001190418,0.001088594,0.0004401652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001845762,0.0007536137,0.002857594,0.002175399,0.0009365525,0.001715034,0.001121258,0.005501966,0.03365526,0.009028254,0.5101736,0.4302358],"study_design_scores_gemma":[0.001746712,0.0008983151,0.01061921,0.0004800582,0.0006998127,0.001831095,0.001434424,0.3556106,0.0613065,0.05457046,0.5103198,0.0004829737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02262648,0.001513989,0.4542176,0.002226181,0.001327183,0.001654209,0.09656007,0.4013244,0.01854993],"genre_scores_gemma":[0.08294195,0.0004377564,0.7246742,0.0009177641,0.0003293901,0.00240667,0.1594988,0.01283606,0.0159574],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03547538,"threshold_uncertainty_score":0.118677,"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."}}