{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003981475,0.0004277721,0.0003207361,0.000656784,0.0004459976,0.0004576204,0.0009575112,0.0002390878,0.002435538],"category_scores_gemma":[0.0002213987,0.0004401622,0.00006324649,0.0004759401,0.0001606854,0.0000309846,0.0002854644,0.0004246184,0.001266328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006431983,"about_ca_system_score_gemma":0.0007926055,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7479553,"about_ca_topic_score_gemma":0.9607055,"domain_scores_codex":[0.9971793,0.00009960247,0.0004433437,0.0009184475,0.0004783943,0.0008809592],"domain_scores_gemma":[0.998447,0.00001520284,0.0001786868,0.0004406864,0.0004714965,0.0004469149],"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":[0.0001930322,0.00009739293,0.0002981183,0.0002275786,0.0001328578,0.0000502849,0.0004207132,0.0001953584,0.0001686948,0.1532139,0.8336993,0.01130277],"study_design_scores_gemma":[0.00008708572,0.000429484,0.0001821187,0.0003284077,0.00001450441,0.000004749162,0.000803483,0.000008478727,0.001685072,0.001503074,0.9943463,0.0006073038],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08768673,0.0026461,0.00003339001,0.02829324,0.005080733,0.008726619,0.001063346,0.0002781985,0.8661916],"genre_scores_gemma":[0.8824269,0.001266587,0.000006494757,0.001299529,0.0006527079,0.000004509774,0.001201547,0.0009108512,0.1122308],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7947402,"threshold_uncertainty_score":0.999805,"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."}}