{"id":"W6920358001","doi":"10.6068/dp14ba85f7ef626","title":"Trend 1986 - 2009. Statistics Canada. CANSIM: Transportation - Transportation by Rail | Country: Canada | Table: Railway transport survey, property accounts summary of assets and accumulated amortization, by mainline companies | Variable: Canadian National, Leasehold improvements, Accumulated amortization | Units: $CAD x 1,000, 1986-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-196.","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; Statistical analysis; Leasehold estate; National accounts; Descriptive statistics; 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":[0.001820297,0.002301834,0.00242983,0.008576812,0.003339963,0.00445565,0.004805529,0.001396136,0.08240158],"category_scores_gemma":[0.01512405,0.001687288,0.001805746,0.04090494,0.0005973132,0.002604208,0.002058151,0.002851613,0.05476859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05289671,"about_ca_system_score_gemma":0.1203691,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9951998,"about_ca_topic_score_gemma":0.9936548,"domain_scores_codex":[0.9959964,0.0002143543,0.0003943306,0.0005350347,0.001901128,0.0009587993],"domain_scores_gemma":[0.9694603,0.0008616996,0.0009240627,0.0008539711,0.02663237,0.001267625],"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":[0.00002370199,0.000007806983,0.001330825,0.0002232195,0.00001805293,0.000007415734,0.0000246627,0.0001277721,0.00001122524,0.0003889429,0.9961531,0.001683231],"study_design_scores_gemma":[0.0001233876,0.00001275077,0.03014614,0.0007402559,0.00005910403,0.00002555916,0.0005466606,0.0004831959,0.0001987361,0.0005049676,0.9670806,0.00007865112],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006612558,0.00004011049,0.00002284198,0.00008556887,0.00002243596,0.00001280202,0.9988335,0.00004745745,0.0008691336],"genre_scores_gemma":[0.0008861619,0.0002415219,0.0003163285,0.00009836357,0.00001503884,0.0001026296,0.9938345,0.00008672065,0.004418811],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08240158,"threshold_uncertainty_score":0.3837942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03286114418262211,"score_gpt":0.2549234837193199,"score_spread":0.2220623395366978,"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."}}