{"id":"W6939228311","doi":"10.6068/dp14ba88e62ea92","title":"Trend 1961 - 2013. Statistics Canada. CANSIM: Construction - Machinery and Equipment | Country: Canada | Table: Flows and stocks of fixed non-residential capital, by sector of North American Industry Classification System (NAICS) and asset | Variable: Geometric (infinite) depreciation, Trucks (x 1,000,000), Public administration, | Units: $CAD Chained (2007) $CAD, 1961-2013. 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; Summary statistics; Stock (firearms); Descriptive statistics; Index (typography); Investment (military); Publication","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":[0.001301021,0.002139686,0.002241157,0.008273757,0.002691989,0.003951541,0.004337696,0.001365233,0.07133794],"category_scores_gemma":[0.01518058,0.001415198,0.001781642,0.03736969,0.0005865875,0.002156136,0.002014038,0.002659606,0.04302665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04178061,"about_ca_system_score_gemma":0.09900914,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9934044,"about_ca_topic_score_gemma":0.9921702,"domain_scores_codex":[0.9969051,0.0001495748,0.0003026596,0.00043684,0.001456558,0.0007491797],"domain_scores_gemma":[0.9739556,0.0009814079,0.000968214,0.0007532032,0.02216011,0.001181604],"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":[0.00002182911,0.00000705199,0.001381655,0.0002120131,0.00002122299,0.00000758032,0.00001943966,0.0001456842,0.000009588218,0.0004218241,0.9963856,0.001366498],"study_design_scores_gemma":[0.000145412,0.00001207528,0.02924097,0.0007259797,0.00006721262,0.00002840711,0.0004634282,0.0005786781,0.0002245668,0.0006909138,0.9677438,0.00007859944],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006540491,0.00004121847,0.00001779046,0.00008494064,0.00001700366,0.000007720253,0.9990966,0.00004084995,0.0006284503],"genre_scores_gemma":[0.0008607507,0.00019872,0.0002145128,0.00008898046,0.00001224441,0.00005843411,0.9955385,0.00005560639,0.002972174],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07133794,"threshold_uncertainty_score":0.3031409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02015519465647585,"score_gpt":0.2367849436654288,"score_spread":0.216629749008953,"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."}}