{"id":"W6920260304","doi":"10.6068/dp14ba87efa5666","title":"Trend 1991 - 2003. Statistics Canada. CANSIM: Construction - Machinery and Equipment | Country: Canada | Table: Capital and repair expenditures, industry sectors 31-33, manufacturing | Variable: Meat product manufacturing (x 1,000,000), Capital, machinery and equipment | Units: $CAD, 1991-2003. 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; Product (mathematics); Official statistics; Capital (architecture); Summary statistics; Manufacturing; Descriptive statistics; Investment (military)","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":[0.001713179,0.002213615,0.00235327,0.007728108,0.003155203,0.004210335,0.004652005,0.001373968,0.08390131],"category_scores_gemma":[0.01494809,0.001545272,0.001871094,0.03453312,0.0005601229,0.002221692,0.002015563,0.002844826,0.04810534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04759277,"about_ca_system_score_gemma":0.1191378,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9949474,"about_ca_topic_score_gemma":0.9933072,"domain_scores_codex":[0.9963725,0.0001994251,0.0003476228,0.000495219,0.00170283,0.0008824924],"domain_scores_gemma":[0.9727739,0.0008937684,0.0009007061,0.0007856443,0.0232949,0.001351102],"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":[0.00002633946,0.000007083304,0.001327178,0.0002411357,0.00002185537,0.000008439691,0.00002332581,0.0001298711,0.00001038677,0.0004107775,0.9959579,0.001835888],"study_design_scores_gemma":[0.0001384466,0.00001245954,0.02782843,0.0007071419,0.00006439761,0.0000308235,0.0004283641,0.000492178,0.0001871363,0.0006091472,0.9694242,0.00007719228],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006920897,0.00005300887,0.00002547357,0.0001106422,0.00002352584,0.00001208095,0.9987158,0.00005131948,0.0009390313],"genre_scores_gemma":[0.001063587,0.0002830222,0.000361541,0.0001362932,0.00001626561,0.00009710683,0.9934694,0.00009827155,0.004474506],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08390131,"threshold_uncertainty_score":0.3453113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01819246330631579,"score_gpt":0.2337901943194957,"score_spread":0.2155977310131799,"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."}}