{"id":"W4253469407","doi":"10.1515/iupac.78.0394","title":"Lot","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; Relation (database); Chemical nomenclature; Computer science; Management science; Data science; Engineering; Chemistry; 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":[0.001129186,0.001970455,0.001256872,0.004655349,0.001011801,0.002894921,0.002578245,0.001893493,0.1527645],"category_scores_gemma":[0.007210829,0.0006545909,0.001330008,0.007969264,0.0004290966,0.003159081,0.002425577,0.001809139,0.1951984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001976023,"about_ca_system_score_gemma":0.002714526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02536489,"about_ca_topic_score_gemma":0.05021485,"domain_scores_codex":[0.9984193,0.0002523464,0.0002765656,0.0005343283,0.0003361611,0.0001813446],"domain_scores_gemma":[0.9968369,0.0007848112,0.0004560546,0.0007614471,0.0009185633,0.0002421825],"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":[0.00005197138,0.00001368161,0.0007981585,0.0006458244,0.00001815839,0.00001575582,0.00002804405,0.0001113128,0.0000887937,0.0007793354,0.994103,0.003345896],"study_design_scores_gemma":[0.00007035584,0.000009952816,0.002153736,0.0003437094,0.00001359459,0.00004769203,0.00008005893,0.0001365383,0.000141518,0.001143695,0.9958396,0.00001964994],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006765337,0.0000586118,0.00006240109,0.00006515251,0.0000159364,0.00001060297,0.9986449,0.0001708494,0.000903886],"genre_scores_gemma":[0.0002279468,0.00006672765,0.000225658,0.00008828293,0.000005505573,0.00006090636,0.9984478,0.00006434632,0.0008127516],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8472354,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179964193685618,"score_gpt":0.4302259540249166,"score_spread":0.4122295346563549,"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."}}