{"id":"W6945457417","doi":"10.25318/3410008201-fra","title":"Dépenses d'immobilisations en construction neuve et en rénovations majeures, selon le type d'actif","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":"Asset (computer security); Capital (architecture); Investment (military); Context (archaeology); 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":[0.000935282,0.001627194,0.001420718,0.008139085,0.001467413,0.002269896,0.003239115,0.001271616,0.03055006],"category_scores_gemma":[0.006479877,0.0009831308,0.00150701,0.01705215,0.0005025352,0.00101344,0.00146537,0.002082468,0.01148242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01550716,"about_ca_system_score_gemma":0.02929621,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9735377,"about_ca_topic_score_gemma":0.9807211,"domain_scores_codex":[0.998372,0.00007081602,0.0001858934,0.000230881,0.0006187069,0.0005216408],"domain_scores_gemma":[0.993103,0.0006119073,0.001263941,0.0002817477,0.004160176,0.0005792615],"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":[0.00009432646,0.00002801473,0.0161367,0.0005462967,0.00007142251,0.00002847314,0.0000882264,0.0003820848,0.00002557196,0.0007246631,0.978357,0.003517308],"study_design_scores_gemma":[0.000479905,0.00003953292,0.3118215,0.0009948751,0.0001853547,0.0001148682,0.0008884989,0.001166328,0.0004992759,0.0004809117,0.6832463,0.00008256548],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007661942,0.00009025993,0.00001848476,0.00006982187,0.00001384765,0.00001306696,0.9982199,0.00004212615,0.0007663107],"genre_scores_gemma":[0.004514181,0.0002989761,0.0001410628,0.00007683938,0.00001584588,0.00009322657,0.9892151,0.00002968412,0.005615187],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03055006,"threshold_uncertainty_score":0.1125128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282481621649924,"score_gpt":0.2889248218787159,"score_spread":0.2761000056622167,"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."}}