{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001007956,0.001267037,0.001334164,0.005227991,0.0009114462,0.002546238,0.002178108,0.001186184,0.03490908],"category_scores_gemma":[0.01105301,0.0007938516,0.001157461,0.01406229,0.0004338206,0.001060872,0.001226333,0.001730909,0.02259279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008688429,"about_ca_system_score_gemma":0.01839105,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.903684,"about_ca_topic_score_gemma":0.9266947,"domain_scores_codex":[0.9987734,0.00008989803,0.0001601695,0.0002423079,0.0004760415,0.0002582308],"domain_scores_gemma":[0.9937347,0.000842308,0.0006771477,0.0004657351,0.003803535,0.0004765583],"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":[0.00004599514,0.00001283956,0.007479857,0.0003478996,0.00004732128,0.00001331341,0.00004177192,0.0004636084,0.00003144294,0.0006242166,0.9886345,0.002257217],"study_design_scores_gemma":[0.0002509923,0.00001628727,0.09069681,0.0006316979,0.00007351212,0.00006856398,0.0004298601,0.001415146,0.0003614892,0.0009206541,0.9050585,0.00007654833],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002298813,0.00004956128,0.00002412944,0.00005955276,0.0000114346,0.000004502544,0.99911,0.0000498099,0.0004610249],"genre_scores_gemma":[0.001665902,0.0001291945,0.0001787868,0.00003909757,0.000008490025,0.00004129778,0.9955165,0.00002661162,0.002394192],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09631604,"threshold_uncertainty_score":0.1937665,"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."}}