{"id":"W4249285801","doi":"10.1515/iupac.79.1512","title":"Isotonic","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":[],"consensus_categories":[],"category_scores_codex":[0.001190404,0.002332916,0.001677417,0.004265619,0.00112071,0.003638043,0.002989074,0.001877935,0.1518182],"category_scores_gemma":[0.009711085,0.0007169816,0.002232943,0.006953637,0.0004289827,0.002572147,0.002803972,0.002110229,0.2104022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001511182,"about_ca_system_score_gemma":0.002972804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02289213,"about_ca_topic_score_gemma":0.04726193,"domain_scores_codex":[0.9980501,0.0003226058,0.0003291456,0.0005973652,0.0004598226,0.0002409907],"domain_scores_gemma":[0.9960176,0.0009030381,0.0004511332,0.00114592,0.001191793,0.0002905122],"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":[0.0001315994,0.00001881241,0.001100993,0.001006247,0.00004245277,0.00001845671,0.00002115459,0.000187842,0.00009463478,0.0007287932,0.9898601,0.006788984],"study_design_scores_gemma":[0.0001812516,0.00002107917,0.003288181,0.0005531112,0.00004614681,0.00006787025,0.0000726747,0.0002667958,0.0002176978,0.001618546,0.9936374,0.00002917081],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001150136,0.0001268203,0.0001074663,0.00007042046,0.00003429455,0.00001830639,0.9975939,0.0003686582,0.001565077],"genre_scores_gemma":[0.0003207349,0.0001144786,0.0003211132,0.0001054855,0.00001202795,0.00007072344,0.9977335,0.0000904847,0.001231425],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1518182,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01548365085922853,"score_gpt":0.4225300287117345,"score_spread":0.407046377852506,"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."}}