{"id":"W4241507154","doi":"10.1515/iupac.88.1481","title":"Ventral","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Philosophy; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001493856,0.001515449,0.001357721,0.003967335,0.001122795,0.004427516,0.002577256,0.00185824,0.2987052],"category_scores_gemma":[0.01448436,0.0006637527,0.001724813,0.007686313,0.0004288492,0.00353043,0.002940249,0.001790946,0.319432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001939955,"about_ca_system_score_gemma":0.003459012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02088168,"about_ca_topic_score_gemma":0.03359177,"domain_scores_codex":[0.9973853,0.0004354081,0.0005225135,0.0007850303,0.0005756851,0.0002960045],"domain_scores_gemma":[0.9938083,0.001523227,0.0006190219,0.001492004,0.002204912,0.0003525295],"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":[0.00007507977,0.00001022216,0.0008084078,0.0008844696,0.00001969647,0.00001322651,0.00002660118,0.00007696227,0.00005403226,0.001044624,0.9899519,0.007034828],"study_design_scores_gemma":[0.0000724444,0.00001010377,0.002074013,0.0006329271,0.00001652679,0.00003999089,0.00007862641,0.00008870244,0.0001065822,0.001282598,0.99558,0.00001753307],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001006617,0.0001195889,0.0001173036,0.0001409952,0.00005725001,0.00002868637,0.9951396,0.0003365454,0.003959405],"genre_scores_gemma":[0.0004158183,0.0001619657,0.0003438723,0.0002249728,0.00001973321,0.0001264313,0.9951476,0.0001487799,0.003410646],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7012948,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02341577853868015,"score_gpt":0.4678743786706519,"score_spread":0.4444586001319717,"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."}}