{"id":"W6938929126","doi":"10.6068/dp14ba82da5f481","title":"Trend 1994 - 2003. Statistics Canada. CANSIM: Construction - Machinery and Equipment | Country: Canada | Table: Capital and repair expenditures, industry sectors 31-33, manufacturing | Variable: Paperboard mills (x 1,000,000), Repair expenditures | Units: $CAD, 1994-2003. 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; Paperboard; Capital (architecture); Summary statistics; Official statistics; Descriptive statistics; Investment (military); Statistical analysis","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":[0.00176209,0.002340222,0.002372748,0.007949937,0.003175078,0.004409456,0.004823872,0.001390329,0.0846246],"category_scores_gemma":[0.01524978,0.001598326,0.001928098,0.03497308,0.0005957173,0.002348544,0.002089338,0.002908617,0.04734546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04955561,"about_ca_system_score_gemma":0.1239628,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9949434,"about_ca_topic_score_gemma":0.9934022,"domain_scores_codex":[0.9962566,0.0002081127,0.0003597982,0.0005071039,0.001768964,0.000899415],"domain_scores_gemma":[0.9723236,0.0008974932,0.0009181789,0.0008239707,0.02366621,0.001370724],"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":[0.0000271294,0.000007402638,0.001294719,0.0002302144,0.00002206187,0.000007998712,0.00002202399,0.0001294313,0.00001014039,0.0004120315,0.9960282,0.001808694],"study_design_scores_gemma":[0.000140769,0.00001235618,0.02530474,0.0007035335,0.00006574384,0.00002821506,0.0004299752,0.0005001306,0.0001879658,0.0006497434,0.9718999,0.00007687568],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006826573,0.00005347242,0.00002575166,0.0001167491,0.00002455123,0.00001251662,0.9987118,0.00005421942,0.0009327735],"genre_scores_gemma":[0.001065234,0.0002823641,0.0003735824,0.0001403166,0.00001649209,0.0001016709,0.9936315,0.0001002259,0.004288547],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0846246,"threshold_uncertainty_score":0.3595528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01596213775537554,"score_gpt":0.2330864342071392,"score_spread":0.2171242964517636,"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."}}