{"id":"W6958082951","doi":"10.6068/dp14ba8bf014f52","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: Electricity meters, electric power production plant (x 1,000,000), Logging and forestry 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; Asset (computer security); Official statistics; Capital (architecture); Investment (military); Capital expenditure; Summary statistics","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":[0.001570866,0.002206794,0.002376425,0.00827481,0.002933339,0.004169025,0.004650022,0.001350992,0.08882596],"category_scores_gemma":[0.01392136,0.001642027,0.001671663,0.03777458,0.000563783,0.002303262,0.00202683,0.002764534,0.05056362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04896907,"about_ca_system_score_gemma":0.1178914,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947124,"about_ca_topic_score_gemma":0.9933023,"domain_scores_codex":[0.996254,0.0001871387,0.0003574267,0.0004796213,0.001816003,0.0009057763],"domain_scores_gemma":[0.9724008,0.000855234,0.0009774832,0.0007240581,0.02371991,0.001322628],"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":[0.00002311706,0.000007087958,0.00132515,0.0002300445,0.00002051376,0.000008031526,0.0000225519,0.0001221149,0.00000932612,0.0004231449,0.9961125,0.001696386],"study_design_scores_gemma":[0.0001293756,0.00001314128,0.03123492,0.0006960727,0.00006135712,0.00002838866,0.0005051301,0.0004702837,0.0001802798,0.0005397916,0.9660707,0.00007049],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007794824,0.00005064619,0.00002212285,0.0001071926,0.00002096269,0.00001197287,0.9986824,0.00004435194,0.0009823664],"genre_scores_gemma":[0.001072945,0.0002827937,0.0002834482,0.0001226006,0.00001519029,0.00009738014,0.9926255,0.00008274103,0.005417399],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08882596,"threshold_uncertainty_score":0.3552971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03366648737267627,"score_gpt":0.2546142410689396,"score_spread":0.2209477536962633,"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."}}