{"id":"W4237210967","doi":"10.1515/iupac.79.2043","title":"Standard","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; Library science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001951245,0.001304271,0.001768549,0.0008022233,0.0002498378,0.0002042771,0.001525686,0.0009834038,0.03245441],"category_scores_gemma":[0.002106623,0.0009779544,0.0005127516,0.000610396,0.0005509533,0.0002662182,0.000584348,0.001237928,0.0003233138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003140008,"about_ca_system_score_gemma":0.003554208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001430913,"about_ca_topic_score_gemma":0.002316239,"domain_scores_codex":[0.991083,0.0003122416,0.001138927,0.001387533,0.004684279,0.001394],"domain_scores_gemma":[0.9935371,0.0002202271,0.0008221329,0.002970971,0.00183771,0.0006119078],"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":[0.001273307,0.0002306092,0.000005512958,0.0001782446,0.000327274,0.0003304953,0.000007577005,8.471352e-7,0.00003032579,0.00001968515,0.9944953,0.003100785],"study_design_scores_gemma":[0.002793029,0.0005265468,0.000009393648,0.0009593928,0.0003605963,0.00005252019,0.00001604521,7.758189e-7,0.00005937814,0.0004242107,0.9934899,0.001308192],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002740004,0.001245051,0.0001373489,0.0005209047,0.001803287,0.0007183083,0.9947533,0.0005541643,0.0002402369],"genre_scores_gemma":[0.0000035477,0.0008195133,0.0001065094,0.0003038385,0.002756826,0.0000408928,0.9944999,0.0003904513,0.001078563],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0321311,"threshold_uncertainty_score":0.9999709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01616573702183687,"score_gpt":0.4211170542259251,"score_spread":0.4049513172040882,"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."}}