{"id":"W4238187662","doi":"10.1515/iupac.79.1256","title":"Excipient","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; Organic chemistry; 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":[],"category_scores_codex":[0.001200401,0.0009209902,0.001095883,0.0005946488,0.0001558179,0.0001091991,0.001146477,0.0006601173,0.0228899],"category_scores_gemma":[0.001210406,0.0006760435,0.0003628155,0.0004163456,0.0003433927,0.0001541413,0.0005104428,0.0008153619,0.00044907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001990819,"about_ca_system_score_gemma":0.001673435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001252003,"about_ca_topic_score_gemma":0.001430187,"domain_scores_codex":[0.993818,0.0001987869,0.0008149248,0.001060609,0.003086355,0.001021251],"domain_scores_gemma":[0.9954157,0.0001329843,0.0005953346,0.002414508,0.0009865558,0.0004549424],"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":[0.0003343068,0.000403282,0.000004253817,0.000108761,0.0001974195,0.0002123343,0.000006695449,7.939706e-7,0.00002784116,0.00001402184,0.9971622,0.001528076],"study_design_scores_gemma":[0.001616751,0.00024155,0.00001960328,0.0007165503,0.0002312891,0.00003790271,0.00001262422,0.000001372311,0.00002283544,0.0001947474,0.9959992,0.0009055737],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003851367,0.0009113149,0.00006567945,0.0003453294,0.001785971,0.0005264981,0.9958693,0.0003350951,0.0001222648],"genre_scores_gemma":[0.000003381787,0.0004851286,0.00005599301,0.0002128323,0.002485825,0.00003774225,0.9953646,0.0002547652,0.00109973],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02244083,"threshold_uncertainty_score":0.9995691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01775737793699741,"score_gpt":0.4278692033894875,"score_spread":0.4101118254524901,"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."}}