{"id":"W6907565745","doi":"10.25318/3310000601-fra","title":"Statistiques financières et fiscales des entreprises, selon le type d'industrie","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":"Context (archaeology); Bad debt; Government (linguistics)","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":[0.001411073,0.00146113,0.001268577,0.006998588,0.000739308,0.002428542,0.001849754,0.001283712,0.02277702],"category_scores_gemma":[0.01091508,0.0007020212,0.001050436,0.01497407,0.0004428907,0.001154389,0.001133605,0.002144675,0.02370321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005470515,"about_ca_system_score_gemma":0.01160554,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5521014,"about_ca_topic_score_gemma":0.6757381,"domain_scores_codex":[0.9984242,0.000144688,0.0002089282,0.0002808852,0.0006276151,0.0003138074],"domain_scores_gemma":[0.9929367,0.001370871,0.001109766,0.0008259204,0.003245868,0.0005109013],"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":[0.00006759357,0.00002028287,0.007033675,0.0004119355,0.00004820542,0.00001720643,0.0000467182,0.0004900529,0.00007165722,0.001005788,0.9877501,0.003036785],"study_design_scores_gemma":[0.0002615681,0.000019715,0.0718052,0.0005272328,0.00006311038,0.00007549651,0.0002371023,0.001079277,0.0003859069,0.001437157,0.9240505,0.00005776474],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002673335,0.0000622829,0.00002403215,0.00005652467,0.000009606366,0.000003283807,0.9991296,0.00005155393,0.0003957769],"genre_scores_gemma":[0.0008898023,0.00009931329,0.0001326323,0.0000202368,0.000007730143,0.00002719835,0.9979029,0.00002071931,0.0008994183],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4478986,"threshold_uncertainty_score":0.9010728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0168968410982729,"score_gpt":0.2897562483228788,"score_spread":0.2728594072246059,"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."}}