{"id":"W6907443695","doi":"10.25318/3410008301-fra","title":"Dépenses d'immobilisations en machines et matériel nouveaux, restaurés/remis à neuf, 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":"Type (biology); Set (abstract data type); Point (geometry); Term (time); Relation (database)","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":[0.001002377,0.001800923,0.001438459,0.005482035,0.001141448,0.0020973,0.002759299,0.00171567,0.04063874],"category_scores_gemma":[0.008748845,0.0008546192,0.001598689,0.01134812,0.0005141179,0.001127454,0.001420905,0.00230878,0.0269289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008953059,"about_ca_system_score_gemma":0.01655931,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8626868,"about_ca_topic_score_gemma":0.918964,"domain_scores_codex":[0.9987289,0.00008558154,0.0001640312,0.0002493946,0.0004381342,0.0003338535],"domain_scores_gemma":[0.9941373,0.0009025091,0.0007067461,0.0004141562,0.003315667,0.0005235311],"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":[0.00004934218,0.00001148509,0.003548936,0.0003052184,0.00002767642,0.00001089342,0.00002281823,0.0001378749,0.0000179639,0.0002485153,0.9942997,0.001319676],"study_design_scores_gemma":[0.0004712304,0.0000244311,0.102426,0.0009080873,0.00009640946,0.00008566456,0.0004118001,0.0007009517,0.0003343768,0.0006977018,0.8937583,0.00008503351],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001444738,0.00004215007,0.00001094019,0.00005381373,0.00001279377,0.000004363626,0.999384,0.00003116921,0.0003162046],"genre_scores_gemma":[0.0009132773,0.00009183166,0.00009794195,0.0000513149,0.000009190529,0.0000489352,0.9969423,0.00002312624,0.001821991],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1373132,"threshold_uncertainty_score":0.2762437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327704671509383,"score_gpt":0.2884056707059645,"score_spread":0.2751286239908706,"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."}}