{"id":"W4251678531","doi":"10.1515/iupac.88.0466","title":"Aneugen","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; Philosophy; Data mining","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":[0.001574132,0.001614637,0.001451541,0.005149388,0.0009589234,0.003694827,0.002255866,0.001859864,0.1855291],"category_scores_gemma":[0.01462654,0.0007607195,0.001681922,0.00832984,0.0004374407,0.002678677,0.002823666,0.002191781,0.1643862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001720861,"about_ca_system_score_gemma":0.003897247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01757558,"about_ca_topic_score_gemma":0.02699992,"domain_scores_codex":[0.9977373,0.0003725344,0.0005411027,0.0006256575,0.0004984415,0.0002249523],"domain_scores_gemma":[0.9937623,0.001966041,0.0008865401,0.001340839,0.001676954,0.0003672289],"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":[0.0001317285,0.00001464833,0.001418284,0.002360711,0.0000475658,0.00002750577,0.00003769308,0.0001459213,0.0001158553,0.001478989,0.9856457,0.008575303],"study_design_scores_gemma":[0.0001213713,0.00001326726,0.002934605,0.001079073,0.00003145533,0.00006551471,0.00005748752,0.0000872124,0.0001316833,0.001341467,0.9941181,0.0000187802],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008785402,0.0001731081,0.00009630829,0.0001028657,0.00004275807,0.00001758864,0.9974259,0.0001732672,0.001880346],"genre_scores_gemma":[0.0003772556,0.0002618103,0.0003530749,0.0001669867,0.00001907773,0.0001177525,0.9969057,0.00009660847,0.001701717],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1855291,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02504942762900918,"score_gpt":0.4707667901758558,"score_spread":0.4457173625468466,"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."}}