{"id":"W4246811231","doi":"10.1515/iupac.88.0698","title":"Duct","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":[0.001838867,0.001667001,0.00145828,0.004487368,0.001127971,0.004391165,0.002834265,0.002050845,0.258257],"category_scores_gemma":[0.01580586,0.0007643113,0.001901268,0.007902337,0.0004536296,0.003856553,0.003118167,0.00196299,0.3091609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001990542,"about_ca_system_score_gemma":0.003770675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01913303,"about_ca_topic_score_gemma":0.02856367,"domain_scores_codex":[0.9968762,0.0005197473,0.0006389014,0.0009377587,0.0006966066,0.0003307441],"domain_scores_gemma":[0.9927394,0.001701845,0.0007717659,0.001878927,0.002532318,0.0003757764],"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":[0.00007238621,0.00001077549,0.0008269181,0.0009582521,0.00002120183,0.00001324579,0.00002551166,0.00008902849,0.00006063427,0.001090049,0.9903785,0.006453553],"study_design_scores_gemma":[0.00008264076,0.0000108552,0.001972081,0.0007015027,0.00001751973,0.00004103463,0.00006835235,0.0001021953,0.0001221887,0.001268782,0.9955943,0.00001853773],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009279501,0.0001210867,0.0001313863,0.000143135,0.00005885565,0.00003238202,0.9956878,0.0003973935,0.00333515],"genre_scores_gemma":[0.0003327229,0.0001530949,0.0003535516,0.0002019533,0.00001833323,0.0001386391,0.9960588,0.0001466975,0.002596164],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.741743,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02677205080936738,"score_gpt":0.4754903957328281,"score_spread":0.4487183449234607,"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."}}