{"id":"W4253532063","doi":"10.1515/iupac.79.1126","title":"Depilatory","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":[0.001491156,0.00178618,0.00174189,0.006012578,0.001020364,0.003936559,0.002400271,0.001554565,0.31451],"category_scores_gemma":[0.01311011,0.0007880316,0.00176614,0.009615685,0.0004156974,0.00275958,0.002551368,0.001970633,0.3755135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001788127,"about_ca_system_score_gemma":0.003236612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01456973,"about_ca_topic_score_gemma":0.02960211,"domain_scores_codex":[0.9977081,0.0003818023,0.0004204968,0.0006367131,0.0005979363,0.0002550065],"domain_scores_gemma":[0.9935197,0.001673558,0.0007272183,0.001497802,0.002073092,0.0005085522],"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":[0.0000325964,0.00000648115,0.0003641564,0.0004997788,0.00001220327,0.000008838883,0.000009596832,0.00006036468,0.00004525196,0.0003305449,0.9953434,0.003286767],"study_design_scores_gemma":[0.00006644824,0.000007348821,0.001440981,0.0003253621,0.00001370845,0.00002991836,0.00003649991,0.0000868134,0.00008752349,0.0006389582,0.9972534,0.00001310654],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004473463,0.0001012767,0.00006575822,0.0000712622,0.00004741362,0.00001608065,0.9977788,0.0002166435,0.001657965],"genre_scores_gemma":[0.0001891714,0.0001325764,0.0002426155,0.0001472139,0.00002291832,0.00009275904,0.9970375,0.0001110021,0.002024289],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.31451,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01623406131481316,"score_gpt":0.4179530230029123,"score_spread":0.4017189616880992,"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."}}