{"id":"W6901516823","doi":"10.6068/dp14ba853a93295","title":"Trend 1991 - 2003. Statistics Canada. CANSIM: Construction - Nonresidential Building Construction | Country: Canada | Table: Capital and repair expenditures, industry sectors 31-33, manufacturing | Variable: Fabric mills (x 1,000,000), Capital, construction | Units: $CAD, 1991-2003. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-035.","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; Capital (architecture); Stock (firearms); Descriptive statistics; Business statistics; Summary 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":[0.001733696,0.002390795,0.002372836,0.008188622,0.003238525,0.00456075,0.004789602,0.00136036,0.091305],"category_scores_gemma":[0.01421519,0.001617489,0.001892198,0.03596277,0.0005790672,0.002334293,0.002113611,0.002831705,0.05360192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05287433,"about_ca_system_score_gemma":0.1263558,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9952792,"about_ca_topic_score_gemma":0.9940058,"domain_scores_codex":[0.9960893,0.0002084322,0.0003517789,0.0005158329,0.001874686,0.0009598321],"domain_scores_gemma":[0.9706044,0.0008499612,0.0009207519,0.0008084459,0.02534151,0.001474874],"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":[0.00002579521,0.000007546967,0.00138616,0.0002280995,0.00002001809,0.000008284185,0.00002436653,0.0001385188,0.00001022394,0.0004344071,0.9956874,0.002029245],"study_design_scores_gemma":[0.0001156307,0.00001179504,0.026768,0.0006630754,0.00005488277,0.00002705928,0.0004468257,0.0005068143,0.000176582,0.0005604054,0.9705951,0.0000737588],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007575402,0.00005352215,0.00002798647,0.0001154983,0.00002436769,0.00001368827,0.9984733,0.00006137713,0.001154409],"genre_scores_gemma":[0.001133328,0.0002967046,0.0003953989,0.0001371285,0.00001670003,0.0001044373,0.9923815,0.0001170217,0.005417781],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.091305,"threshold_uncertainty_score":0.3836319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01500829040446048,"score_gpt":0.2323601570670245,"score_spread":0.217351866662564,"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."}}