{"id":"W6945407045","doi":"10.25318/3610005901-fra","title":"Opérations internationales en valeurs mobilières, opérations de portefeuille, ventes et achats, nets et brutes, par catégorie et secteur, trimestriel","year":2019,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Flux (metallurgy); Administration (probate law); Value (mathematics)","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":[0.0008294093,0.001568785,0.001240639,0.009221134,0.0009292655,0.002891768,0.001643647,0.0008359056,0.05021469],"category_scores_gemma":[0.008763508,0.0006964795,0.0009535204,0.026244,0.0004407552,0.001442107,0.001194309,0.001829842,0.0303702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007377113,"about_ca_system_score_gemma":0.01485033,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6436311,"about_ca_topic_score_gemma":0.687815,"domain_scores_codex":[0.9982916,0.000118909,0.0002327816,0.0003738432,0.0006563198,0.0003266028],"domain_scores_gemma":[0.9945546,0.001021543,0.0006021642,0.0005453411,0.002952702,0.000323524],"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":[0.00003066747,0.00000926616,0.003065867,0.0005511367,0.00002703656,0.00001056167,0.00003478505,0.0002674978,0.00004221409,0.001097557,0.9915553,0.003308224],"study_design_scores_gemma":[0.0000653689,0.000004252505,0.01562529,0.0003347853,0.00001987026,0.00002234241,0.0001325563,0.000189466,0.0001646317,0.0005737481,0.9828452,0.00002253816],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000959847,0.00005368912,0.00002437271,0.00003078747,0.00001120079,0.000004349045,0.9986736,0.00005573753,0.001050357],"genre_scores_gemma":[0.0006528877,0.0001579688,0.0001717033,0.00002599256,0.000006765553,0.00003428604,0.9976216,0.00003541646,0.001293394],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3563689,"threshold_uncertainty_score":0.7169353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01607654207441382,"score_gpt":0.2885367575113013,"score_spread":0.2724602154368875,"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."}}