{"id":"W4230006699","doi":"10.1515/iupac.79.0765","title":"Adaptation","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; Multidisciplinary approach; Hazard; Toxicology; Chemistry; Biology; Philosophy; Linguistics; Sociology; Social science","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":[],"consensus_categories":[],"category_scores_codex":[0.003081769,0.002266974,0.001656604,0.004750797,0.001262204,0.004229018,0.00385184,0.001964272,0.1951153],"category_scores_gemma":[0.02199235,0.0008928308,0.002912329,0.008735758,0.0005481573,0.003532865,0.00333489,0.002636743,0.2705562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001958062,"about_ca_system_score_gemma":0.003513122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02170035,"about_ca_topic_score_gemma":0.04142227,"domain_scores_codex":[0.9958651,0.0008848317,0.0007724646,0.001353047,0.0006958238,0.0004287872],"domain_scores_gemma":[0.9918591,0.002041921,0.0005333242,0.002870092,0.0022246,0.0004709753],"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":[0.00009985393,0.00001834383,0.000835167,0.0006561017,0.00004498619,0.00001777294,0.00002558237,0.0001724462,0.00006812298,0.0005522571,0.9920726,0.005436731],"study_design_scores_gemma":[0.0001765456,0.00001624383,0.002795548,0.0004369153,0.00003880464,0.00005640374,0.00007893424,0.0002587567,0.0001494064,0.001472813,0.994486,0.00003354625],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001193705,0.00007323194,0.0001722904,0.000106492,0.00007979573,0.00004001269,0.997133,0.0005765305,0.001699295],"genre_scores_gemma":[0.0002914572,0.00006803797,0.0005203443,0.0001153472,0.00001468619,0.0002125561,0.9970785,0.0001652824,0.001533657],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1951153,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02447867762867372,"score_gpt":0.4218612070505406,"score_spread":0.3973825294218669,"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."}}