{"id":"W4254667210","doi":"10.1515/iupac.78.0422","title":"Model","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Glossary; Chemical nomenclature; Relation (database); Computer science; Management science; Data science; Environmental chemistry; Chemistry; Engineering; Data mining; Linguistics","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":[0.001362354,0.002162796,0.001024974,0.003572394,0.0008958754,0.00297677,0.004062846,0.002026769,0.1457644],"category_scores_gemma":[0.008924684,0.0005907208,0.00217365,0.005546931,0.0003755822,0.002695196,0.001693058,0.00223373,0.1528669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002404453,"about_ca_system_score_gemma":0.002785659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04103522,"about_ca_topic_score_gemma":0.07273506,"domain_scores_codex":[0.9984309,0.0003148263,0.0002200637,0.0005634915,0.0003027392,0.0001678358],"domain_scores_gemma":[0.9969579,0.001071085,0.0002518336,0.0006889368,0.0008656809,0.0001645864],"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":[0.00006637117,0.0000248438,0.001472038,0.0005225087,0.00004144029,0.00002729708,0.00002316982,0.001260027,0.00007725898,0.001678803,0.9882659,0.006540182],"study_design_scores_gemma":[0.0001346512,0.00001971253,0.002144822,0.0004389778,0.00003446873,0.00007774221,0.00009313855,0.002595062,0.0001868984,0.004917465,0.989326,0.00003104549],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001691181,0.0001080425,0.0003924134,0.0001916571,0.00003477245,0.00002608818,0.9969152,0.0006093886,0.001553243],"genre_scores_gemma":[0.000507656,0.00008988323,0.0007962492,0.0001080981,0.000007734528,0.0001140665,0.9970587,0.00009667768,0.001220842],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8542356,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02764981446797459,"score_gpt":0.4337168715578972,"score_spread":0.4060670570899226,"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."}}