{"id":"W6907632794","doi":"10.25318/1810013201-fra","title":"Indices des prix de l'industrie pour appareils électriques et de télécommunication, produits minéraux nonmétalliques, produits du pétrole et charbon (1992=100)","year":2020,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"General interest; Population; State supreme court","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008552196,0.001663658,0.001311022,0.00637479,0.0008746152,0.002075573,0.001936631,0.001082814,0.02426341],"category_scores_gemma":[0.005545166,0.0006077054,0.00108185,0.0159128,0.0004054878,0.0008992284,0.0009564264,0.001767408,0.02159815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008065165,"about_ca_system_score_gemma":0.01254181,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7958002,"about_ca_topic_score_gemma":0.8455,"domain_scores_codex":[0.9989653,0.00008024123,0.0001176323,0.0002373138,0.0003787189,0.0002208003],"domain_scores_gemma":[0.9956337,0.0006458806,0.0004574874,0.0003182031,0.002637932,0.0003067188],"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":[0.00007302682,0.00002140584,0.007986607,0.0007151114,0.00005444809,0.0000201019,0.00004081868,0.0004207888,0.0000610276,0.0004628297,0.9869343,0.003209579],"study_design_scores_gemma":[0.0002264769,0.00002277936,0.1133354,0.0005042456,0.00008128852,0.00006611168,0.0004374066,0.0006463365,0.000387611,0.0004293949,0.8838087,0.0000543087],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003124758,0.00006947479,0.00001477553,0.00003964478,0.000009210891,0.000004138167,0.9990605,0.0000337732,0.0004560111],"genre_scores_gemma":[0.001222992,0.000143623,0.0001111816,0.00002256215,0.000006377697,0.00003337372,0.9969361,0.00001506251,0.001508729],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2041998,"threshold_uncertainty_score":0.4108047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01800828325794383,"score_gpt":0.3050213115498827,"score_spread":0.2870130282919388,"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."}}