{"id":"W4238601645","doi":"10.1515/iupac.88.0838","title":"Gene","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Data mining; Philosophy","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":[0.001401079,0.001627408,0.001496395,0.003991901,0.001136557,0.004047157,0.002919083,0.002234151,0.2316397],"category_scores_gemma":[0.01284901,0.0007565154,0.001733027,0.007585809,0.0004859906,0.003069142,0.002583186,0.001937704,0.2416704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001810012,"about_ca_system_score_gemma":0.00347478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0186143,"about_ca_topic_score_gemma":0.03088276,"domain_scores_codex":[0.9978203,0.0003333666,0.0004137759,0.0007269746,0.0004621664,0.0002434649],"domain_scores_gemma":[0.9949174,0.001518469,0.0005410886,0.001214756,0.001537905,0.0002703918],"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":[0.000103693,0.00001219238,0.001284923,0.001363682,0.00003121041,0.00002274097,0.00004118266,0.0001265914,0.0001045204,0.001274124,0.9882289,0.007406172],"study_design_scores_gemma":[0.0001044823,0.00001315919,0.002839616,0.0007714243,0.00002815139,0.00006636813,0.00007504031,0.0001169952,0.0001517613,0.001820456,0.9939901,0.00002262602],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009027654,0.0001405619,0.0001287533,0.000143247,0.00004169672,0.00002001632,0.9969336,0.0002871252,0.002214862],"genre_scores_gemma":[0.0003675928,0.0001832474,0.0004011365,0.0002191441,0.00001395907,0.0001177511,0.9967344,0.0001285606,0.00183409],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2316397,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02602469250439727,"score_gpt":0.4587141569206476,"score_spread":0.4326894644162503,"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."}}