{"id":"W6920486649","doi":"10.6068/dp14ba85a28dc20","title":"Trend 1994 - 1996. Statistics Canada. CANSIM: Construction - Machinery and Equipment | Country: Canada | Table: Capital expenditures on machinery and equipment, by type of asset and Standard Industrial Classification (1980 SIC) | Variable: Buses, all types (x 1,000,000), Finance and insurance industries | Units: $CAD, 1994-1996. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-034.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Census; Official statistics; Asset (computer security); Summary statistics; Capital (architecture); Investment (military); Capital expenditure; Index (typography)","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":[0.001750521,0.00235488,0.00242483,0.00868319,0.003102788,0.004493976,0.004960322,0.001364114,0.09944447],"category_scores_gemma":[0.01543034,0.001693651,0.001722228,0.03740604,0.0005782427,0.002500193,0.002128978,0.002889059,0.05989061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04935988,"about_ca_system_score_gemma":0.1155615,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9937804,"about_ca_topic_score_gemma":0.9920393,"domain_scores_codex":[0.9959546,0.0002078505,0.000390473,0.0005322453,0.001989146,0.0009256295],"domain_scores_gemma":[0.9703189,0.0009494095,0.00103471,0.0008655007,0.02538518,0.001446354],"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":[0.00002268769,0.000006793343,0.001108841,0.0001994778,0.00001793505,0.000007364624,0.00001999216,0.0001131991,0.000008745039,0.0003967818,0.9964038,0.001694359],"study_design_scores_gemma":[0.0001202432,0.00001118174,0.02380541,0.0006083552,0.0000503573,0.00002261154,0.0003942745,0.0003983619,0.0001653939,0.000533242,0.9738262,0.0000643397],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006281456,0.00004404556,0.00002302736,0.0001004229,0.00002089415,0.00001277158,0.9986771,0.00004801589,0.001010975],"genre_scores_gemma":[0.0008421653,0.0002476981,0.0002949708,0.0001066457,0.00001436044,0.0001022347,0.9931015,0.00008643351,0.005203935],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09944447,"threshold_uncertainty_score":0.3581327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04022417258427948,"score_gpt":0.2637762762168884,"score_spread":0.2235521036326089,"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."}}