{"id":"W4243171249","doi":"10.1515/iupac.79.1277","title":"External Validity","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Multidisciplinary approach; Computer science; Toxicology; Chemistry; Philosophy; Biology; Political science; Linguistics; Law; 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":[0.03933879,0.002219792,0.003458876,0.00624036,0.002064468,0.005263915,0.005084066,0.003719868,0.1661398],"category_scores_gemma":[0.2926323,0.001074943,0.005735354,0.01017432,0.002116337,0.004716099,0.004013837,0.004078051,0.0421189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004367631,"about_ca_system_score_gemma":0.01230733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008143182,"about_ca_topic_score_gemma":0.01189085,"domain_scores_codex":[0.9411709,0.0191898,0.01660818,0.01289506,0.008212687,0.001923389],"domain_scores_gemma":[0.8021881,0.1134664,0.01509426,0.03053036,0.03705191,0.00166893],"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":[0.001272084,0.0001992849,0.02336307,0.01716297,0.001359305,0.0001672576,0.0005014137,0.001367238,0.0001193577,0.009425289,0.882974,0.06208866],"study_design_scores_gemma":[0.003891662,0.0002110709,0.02652218,0.01395772,0.001939637,0.0003702679,0.0006040719,0.001778303,0.0006103748,0.0301958,0.9196889,0.0002300462],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003908252,0.00236295,0.006327237,0.002380331,0.001043997,0.005989195,0.9559138,0.0007535511,0.0213207],"genre_scores_gemma":[0.03600121,0.001483447,0.01763113,0.004944507,0.0007453085,0.07211501,0.852095,0.0009616151,0.01402265],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1661398,"threshold_uncertainty_score":0.555793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02575322199614691,"score_gpt":0.4047558225864892,"score_spread":0.3790026005903423,"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."}}