{"id":"W7035836512","doi":"","title":"Advances in artificial intelligence: 32nd Canadian conference on artificial intelligence, Canadian AI 2019, Kingston, ON, Canada, May 28-31, 2019, proceedings","year":2019,"lang":"en","type":"other","venue":"CERN Document Server (European Organization for Nuclear Research)","topic":"Sesquiterpenes and Asteraceae Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Applications of artificial intelligence; Government (linguistics); MEDLINE","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00222821,0.0014376,0.001258068,0.002922994,0.002047461,0.007592154,0.001617294,0.001345411,0.1989435],"category_scores_gemma":[0.004267296,0.0005098391,0.0006603008,0.004620492,0.001728531,0.00272479,0.00223758,0.002550764,0.06586081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01179368,"about_ca_system_score_gemma":0.02629535,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4726852,"about_ca_topic_score_gemma":0.6905166,"domain_scores_codex":[0.9985811,0.0001034138,0.00006355337,0.0001306561,0.0009569669,0.000164373],"domain_scores_gemma":[0.9956252,0.0004328297,0.00007536272,0.0003142204,0.002881963,0.0006704584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002717207,0.00002195372,0.0001624417,0.000135787,0.00001014114,0.0000134492,0.00002594736,0.0002617965,0.0001721642,0.005072445,0.9135145,0.08058211],"study_design_scores_gemma":[0.000007702587,0.000006006464,0.0007979363,0.0001160423,0.00001029664,0.00002240026,0.00007093687,0.0005832146,0.0002206231,0.003404083,0.9947489,0.00001186412],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002018875,0.07875976,0.01840387,0.02964056,0.02586045,0.0002652144,0.01336416,0.003121211,0.8285659],"genre_scores_gemma":[0.008226356,0.04176726,0.009589097,0.00106201,0.001371728,0.00006598168,0.008611656,0.000811117,0.9284948],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5273148,"threshold_uncertainty_score":0.9398677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0230810957789312,"score_gpt":0.28202073887766,"score_spread":0.2589396430987289,"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."}}