{"id":"W6901604610","doi":"10.6068/dp14baa275af845","title":"Trend 1998 - 2000. Statistics Canada. CANSIM: Labor - Industries | Country: Canada | Province: Alberta | Table: Construction industries, revenues and expenses, principal statistics by North American Industry Classification System (NAICS) | Variable: Building construction, Cost of construction materials and supplies | Units: $CAD x 1,000, 1998-2000. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-139.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Legal Cases and Commentary","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Census; Official statistics; Revenue; Summary statistics; Descriptive statistics; Principal (computer security); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000323946,0.0007625752,0.001407134,0.0001404599,0.0002497149,0.0002584592,0.0005208628,0.0006743163,0.0003288554],"category_scores_gemma":[0.0001900759,0.000759864,2.519245e-7,0.0004934502,0.001431785,0.0003129327,0.0003628482,0.001207828,0.000001910135],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008090396,"about_ca_system_score_gemma":0.0211238,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.998983,"about_ca_topic_score_gemma":0.9892582,"domain_scores_codex":[0.995611,0.0003838776,0.001347734,0.001070085,0.0009584266,0.0006288805],"domain_scores_gemma":[0.9954241,0.0005893147,0.001794819,0.001430274,0.0001662493,0.0005952498],"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":[0.0003431855,0.00004674177,0.002096631,0.001354716,0.000421816,0.0001529698,0.000007136628,0.000001934178,0.00002849684,0.00146717,0.9926116,0.001467564],"study_design_scores_gemma":[0.00128416,0.0001819709,0.00004631484,0.0002144326,0.001033603,0.001690274,0.002789143,0.0001711011,0.000002923469,1.540154e-7,0.9918999,0.0006860396],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002742821,0.0009964966,0.00001696754,0.00001613537,0.0008411373,0.001196005,0.9964016,0.00005513337,0.0002022274],"genre_scores_gemma":[0.0004576476,0.0009587959,0.001030981,0.0001648098,0.0003395953,0.00004194799,0.9962855,0.0001630588,0.0005576949],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02031476,"threshold_uncertainty_score":0.9994853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01907066282416495,"score_gpt":0.2419155473229027,"score_spread":0.2228448844987377,"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."}}