{"id":"W4250062833","doi":"10.1515/iupac.88.0781","title":"Expression, Gene","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Effects and risks of endocrine disrupting chemicals","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Expression (computer science); Relation (database); Biology; 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":[0.001239073,0.001790078,0.0016452,0.004763873,0.001035767,0.003604762,0.002031404,0.001737729,0.1177085],"category_scores_gemma":[0.01028539,0.0006629264,0.001823399,0.009938234,0.0006640678,0.002573837,0.002194058,0.002152259,0.1022463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001243972,"about_ca_system_score_gemma":0.002602729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01249481,"about_ca_topic_score_gemma":0.01942865,"domain_scores_codex":[0.9979215,0.0002594609,0.0003987215,0.0008278571,0.0004006682,0.000191762],"domain_scores_gemma":[0.9967532,0.00127933,0.000430957,0.0008398815,0.000556547,0.0001401072],"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":[0.0001601708,0.00002311457,0.003804696,0.004185831,0.0001009761,0.00006363356,0.00008216447,0.0004574553,0.0007346071,0.0016543,0.9718558,0.01687729],"study_design_scores_gemma":[0.00009332826,0.00002209596,0.006081732,0.0008459176,0.0000795455,0.0001611389,0.00008400004,0.0002356107,0.0003557605,0.00284816,0.9891558,0.00003690949],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000178833,0.0005682656,0.00033411,0.0001180688,0.00008107918,0.00002195953,0.9964127,0.0004054375,0.001879556],"genre_scores_gemma":[0.0008668536,0.0006192235,0.001067498,0.0002843091,0.00002572513,0.0001982652,0.9952455,0.0001641101,0.001528468],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1177085,"threshold_uncertainty_score":0.3937742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218403297391427,"score_gpt":0.447817289865079,"score_spread":0.4356332568911647,"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."}}