{"id":"W6976506612","doi":"10.6068/dp14ba8beff7b51","title":"Trend 1992 - 1997. 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: Material handling equipment, conventional (x 1,000,000), Mining, quarrying and oil well industries | Units: $CAD, 1992-1997. 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); Capital (architecture); Summary statistics; Capital expenditure; Investment (military); 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":[0.001462265,0.002179155,0.002293654,0.008315161,0.002791742,0.004136142,0.004491722,0.00130871,0.07706007],"category_scores_gemma":[0.01357694,0.0015494,0.001609674,0.03748864,0.0005719129,0.002214907,0.001962148,0.002747936,0.04862696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04375315,"about_ca_system_score_gemma":0.105024,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9935243,"about_ca_topic_score_gemma":0.9924515,"domain_scores_codex":[0.9965443,0.000171686,0.0003305192,0.0004775825,0.001653928,0.0008220588],"domain_scores_gemma":[0.9739887,0.0008714879,0.0009858207,0.0007232937,0.02219714,0.001233552],"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":[0.00002277142,0.000007007784,0.001344169,0.0002219545,0.00002010735,0.000008105971,0.00002126899,0.0001218309,0.000009142645,0.0003799393,0.9963592,0.001484575],"study_design_scores_gemma":[0.0001336367,0.00001276154,0.02952231,0.0006544015,0.00005841218,0.00002831106,0.0004643413,0.0004576946,0.0001780351,0.0005093907,0.9679139,0.00006679022],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006837903,0.00004384308,0.00001754012,0.00008475018,0.00001694042,0.000009418573,0.9989986,0.00003783462,0.0007227557],"genre_scores_gemma":[0.0008071336,0.0002096592,0.0002047337,0.00008595661,0.00001193719,0.00007063926,0.995017,0.00005807873,0.003534902],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07706007,"threshold_uncertainty_score":0.3174528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04213893873135208,"score_gpt":0.2679236335641168,"score_spread":0.2257846948327647,"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."}}