{"id":"W4233112298","doi":"10.1515/iupac.79.1849","title":"Preneoplastic","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Chemistry and Chemical Engineering","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Hazard; Toxicology; Chemistry; Biology; Philosophy; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009354793,0.0002742258,0.0002540451,0.00001528286,0.00004164889,0.00001774332,0.0003663347,0.0002420651,0.03487002],"category_scores_gemma":[0.0002452055,0.0002105046,0.00008698471,0.00009224878,0.0001229524,0.00005015989,0.0002915209,0.0002878406,0.00002180972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004467268,"about_ca_system_score_gemma":0.00004130699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003378225,"about_ca_topic_score_gemma":0.00006151083,"domain_scores_codex":[0.9984651,0.000007524643,0.0002165376,0.000373171,0.0006254138,0.0003122199],"domain_scores_gemma":[0.9992269,0.00005960757,0.00007366503,0.0004589895,0.00001202302,0.0001688323],"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":[0.00001828414,0.00005373032,0.000005897407,0.00005648201,0.00001424072,0.00002167173,0.000001591893,0.00006662958,0.001340504,1.869967e-7,0.9975434,0.0008773978],"study_design_scores_gemma":[0.0002481875,0.00002339824,0.000009724185,0.0001310749,0.0000338166,0.00001273918,0.000001221866,0.00001657159,0.001637026,0.00004251748,0.9975486,0.0002950681],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000512318,0.0000558972,0.0001490378,0.00007568843,0.0002074735,0.00006254713,0.998534,0.00005182379,0.0003511779],"genre_scores_gemma":[0.0001649754,0.0001069949,0.00003052968,0.00005798621,0.0003645432,0.000007652807,0.9984585,0.00001933356,0.0007894811],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03484821,"threshold_uncertainty_score":0.9660122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004748441607859181,"score_gpt":0.3116900307618805,"score_spread":0.3069415891540213,"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."}}