{"id":"W6907914094","doi":"10.25318/3410023701-fra","title":"Stocks d'actifs relatifs aux déchets solides de propriété municipale, selon la zone (urbaine ou rurale) et la taille de population, Infrastructure Canada, inactif","year":2019,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Population; Investment (military); Stock (firearms)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.002108689,0.001902837,0.001656387,0.0006847853,0.001035881,0.00054323,0.001342138,0.001622014,0.000794673],"category_scores_gemma":[0.00747243,0.002280378,0.0001224422,0.001377579,0.0003704619,0.0007134679,0.0003922171,0.003627566,0.00005585817],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.02213432,"about_ca_system_score_gemma":0.03221804,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9930803,"about_ca_topic_score_gemma":0.9996817,"domain_scores_codex":[0.9883869,0.002681742,0.002115803,0.00153693,0.003105768,0.002172896],"domain_scores_gemma":[0.9877786,0.004533478,0.002856229,0.001901423,0.001769068,0.001161212],"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":[0.0002338795,0.0001881361,0.008279561,0.001755732,0.0005007316,0.001006631,0.001302852,0.01523398,0.0002991711,0.004851292,0.9606956,0.005652455],"study_design_scores_gemma":[0.001068096,0.0001386788,0.3476907,0.001428727,0.00112166,0.0004828794,0.001919775,0.01297547,0.0001387835,0.0008037932,0.6300049,0.002226542],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02653136,0.0005498012,0.004531176,0.000810466,0.001522434,0.002107339,0.9634987,0.00008778406,0.0003609558],"genre_scores_gemma":[0.160704,0.0002212354,0.008491387,0.0002946845,0.000237724,0.0001885536,0.8184227,0.0005103446,0.01092938],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3394111,"threshold_uncertainty_score":0.9996741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007344416290242592,"score_gpt":0.2833445231863889,"score_spread":0.2760001068961463,"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."}}