{"id":"W4239699144","doi":"10.1515/iupac.79.1139","title":"Detriment","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 Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Chemistry; Philosophy; Biology; 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":[0.001278552,0.001397591,0.001479369,0.00383317,0.001276145,0.003499406,0.002356946,0.001656236,0.1816259],"category_scores_gemma":[0.01442451,0.0005207162,0.002220853,0.005818147,0.0004128864,0.002803444,0.002120709,0.001929555,0.1376514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001706595,"about_ca_system_score_gemma":0.002762392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02476703,"about_ca_topic_score_gemma":0.03783232,"domain_scores_codex":[0.997242,0.0004166976,0.0004973102,0.0008682077,0.0006453707,0.0003304583],"domain_scores_gemma":[0.9943075,0.001656776,0.0008215327,0.001342972,0.001583043,0.0002881951],"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":[0.0002052698,0.00002683479,0.004147575,0.001548073,0.00008049583,0.00003978381,0.00004413437,0.000248973,0.00009598469,0.00193234,0.9725323,0.01909824],"study_design_scores_gemma":[0.0001184815,0.00001890786,0.005853427,0.000693055,0.00006244661,0.0001327285,0.0001210687,0.0002141416,0.0001616151,0.001838152,0.9907615,0.00002451107],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006317923,0.0005775421,0.0002462188,0.000295384,0.0001306648,0.00005114934,0.9887477,0.000344996,0.008974528],"genre_scores_gemma":[0.002928201,0.0006008909,0.0007093232,0.0007728642,0.00007555899,0.0002228703,0.9862895,0.0001909643,0.008209814],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.818374,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01793568243371289,"score_gpt":0.4343387072400295,"score_spread":0.4164030248063166,"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."}}