{"id":"W6957931210","doi":"10.6068/dp14ba8bc0bdb39","title":"Trend 1998 - 2011. Statistics Canada. CANSIM: Construction - Residential Construction | Country: Canada | Table: Capital expenditures on construction, by type of asset and North American Industry Classification System (NAICS) sector | Variable: Breakwaters (x 1,000,000), Administrative and support, waste management and remediation services | Units: $CAD, 1998-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-037.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Engineering Education and Curriculum Development","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Census; Official statistics; Summary statistics; Stock (firearms); Asset (computer security); Index (typography); Descriptive statistics; Capital (architecture)","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":[0.0001544867,0.0004501091,0.000498148,0.0001192004,0.00009240519,0.0001259916,0.0003018513,0.0002692227,0.0002486483],"category_scores_gemma":[0.000005816249,0.0004648246,1.7487e-7,0.000123337,0.000239069,0.0002021688,0.00009174123,0.0004492663,0.000004603372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002491274,"about_ca_system_score_gemma":0.001159715,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9636069,"about_ca_topic_score_gemma":0.939401,"domain_scores_codex":[0.997802,0.0001099834,0.0006196325,0.0006150163,0.0005453498,0.0003080648],"domain_scores_gemma":[0.9983965,0.0001049107,0.000520603,0.0006234932,0.00004897428,0.000305586],"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":[0.00003559902,0.00001473903,0.000277885,0.001248106,0.0003026326,0.0000226651,0.00001284019,0.00006071118,0.000006335509,0.0002907557,0.9974623,0.0002654149],"study_design_scores_gemma":[0.0004041628,0.00006302849,0.0001689864,0.00005302469,0.000250122,0.0002341698,0.004370349,0.001875152,6.817944e-7,1.000186e-7,0.9921206,0.0004596517],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002317913,0.0003561396,0.00001761234,0.000001766556,0.001486897,0.0004078446,0.9969537,0.00007849443,0.0004658099],"genre_scores_gemma":[0.001921768,0.0008543799,0.0004689504,0.00002007112,0.0001354217,0.00002013887,0.9962987,0.00008544308,0.0001950798],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02420584,"threshold_uncertainty_score":0.9997804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01379914220922565,"score_gpt":0.2201036848880858,"score_spread":0.2063045426788601,"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."}}