{"id":"W4237396860","doi":"10.1515/iupac.79.2014","title":"Sign","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; Linguistics; Biology","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":[],"category_scores_codex":[0.001313553,0.0009156775,0.001127287,0.0005612848,0.0001499512,0.0001147571,0.001184421,0.000750641,0.02551338],"category_scores_gemma":[0.001613603,0.0006792875,0.0003384507,0.0004304714,0.0003404959,0.0001742369,0.0003829083,0.0008628132,0.0005094547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00152046,"about_ca_system_score_gemma":0.001998434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001570555,"about_ca_topic_score_gemma":0.001277312,"domain_scores_codex":[0.994235,0.0002460889,0.0007603486,0.001002865,0.002786475,0.0009691997],"domain_scores_gemma":[0.9955816,0.0002013186,0.0005709768,0.002250244,0.0009901193,0.0004057148],"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":[0.0003572307,0.0003004556,0.000002194725,0.0001113132,0.0002105082,0.0002339741,0.000004647147,6.740634e-7,0.00005288708,0.000009548005,0.9962468,0.002469834],"study_design_scores_gemma":[0.001571237,0.0002536028,0.00001129085,0.0006065432,0.0002520962,0.00003303651,0.000008601767,0.000001110521,0.00003504597,0.0002465558,0.9960517,0.0009291997],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001304938,0.000846103,0.00007800222,0.0003096877,0.001177067,0.0005079014,0.9965177,0.0003727407,0.0001777045],"genre_scores_gemma":[0.000003549368,0.0003883218,0.00005657698,0.0002671478,0.002575744,0.00002893115,0.995189,0.0002826676,0.001208096],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02500392,"threshold_uncertainty_score":0.9995658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779851327244912,"score_gpt":0.4200181970425819,"score_spread":0.4022196837701327,"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."}}