{"id":"W4249327167","doi":"10.1515/iupac.79.1319","title":"Gene","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; Multidisciplinary approach; Toxicology; Library science; Chemistry; Biology; Philosophy; Linguistics; Sociology; Social science; Organic chemistry","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":[0.001300523,0.002020167,0.001694453,0.003324958,0.001160708,0.003753154,0.00316252,0.002154312,0.1868298],"category_scores_gemma":[0.00959921,0.0006550187,0.001979722,0.005906868,0.0004191859,0.002318979,0.002115316,0.001825881,0.2383639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00158784,"about_ca_system_score_gemma":0.002795843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01919303,"about_ca_topic_score_gemma":0.0348642,"domain_scores_codex":[0.9981191,0.0003028869,0.0002647307,0.0007338245,0.0003597196,0.0002198111],"domain_scores_gemma":[0.9966561,0.0009192589,0.0003402919,0.0009036497,0.0009575751,0.0002230897],"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":[0.0001315861,0.00001817688,0.001430653,0.0009882208,0.00004753148,0.00002412643,0.00002991852,0.0002005007,0.0001089615,0.0007996779,0.9898981,0.006322545],"study_design_scores_gemma":[0.0001745215,0.00001961157,0.002967961,0.000512355,0.00005266117,0.00007496334,0.00007254728,0.0002283813,0.0001998652,0.001932036,0.9937378,0.00002730171],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009712212,0.0001044067,0.0001069058,0.00008613352,0.00002850556,0.00001546547,0.9980676,0.0003151802,0.001178716],"genre_scores_gemma":[0.0003134591,0.0001025326,0.0003531262,0.0001449299,0.00001059625,0.00010015,0.9976612,0.0001018857,0.001212027],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1868298,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01781412621363107,"score_gpt":0.4176834719828241,"score_spread":0.399869345769193,"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."}}