{"id":"W6945203740","doi":"10.25318/3610023601-eng","title":"Flows and stocks of fixed non-residential capital, by North American Industry Classification System (NAICS) and asset, Canada, provinces and territories","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Wood and Agarwood Research","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stock (firearms); Table (database); Asset (computer security); Time series; Flow (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":[0.0005182415,0.001024947,0.001040954,0.004976635,0.0007523685,0.001767547,0.00177446,0.0007060896,0.02729915],"category_scores_gemma":[0.005607074,0.0005217718,0.0008725795,0.01396898,0.0002905254,0.0008836687,0.000839314,0.001481334,0.01790699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004930524,"about_ca_system_score_gemma":0.01030183,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7607995,"about_ca_topic_score_gemma":0.8003023,"domain_scores_codex":[0.999241,0.00003867819,0.00008511852,0.0001800755,0.0002794588,0.0001756936],"domain_scores_gemma":[0.9960872,0.0004278439,0.0005495129,0.0003008367,0.002339854,0.0002947104],"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":[0.00003776404,0.00001361505,0.009029707,0.0002240485,0.00004647932,0.00001337665,0.00002021017,0.0003214238,0.00002562938,0.0006920926,0.9876481,0.001927483],"study_design_scores_gemma":[0.0001904747,0.0000180026,0.1209205,0.0005522236,0.00009700102,0.00007186189,0.0003323789,0.001120624,0.0003693632,0.00100815,0.8752598,0.0000596725],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003774835,0.00005527395,0.00001851329,0.00004429801,0.00001173254,0.000004022617,0.9988731,0.00003222816,0.0005834041],"genre_scores_gemma":[0.001706956,0.0001342564,0.0001032422,0.00003096637,0.000009069719,0.00003034606,0.9961449,0.0000173368,0.0018229],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2392005,"threshold_uncertainty_score":0.4812183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005925926590982804,"score_gpt":0.2470597417572287,"score_spread":0.2411338151662459,"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."}}