{"id":"W6957631725","doi":"10.6068/dp14ba85465db65","title":"Trend 1994 - 2013. Statistics Canada. CANSIM: Seniors - Work and Retirement | Country: Canada | Table: Business enterprise research and development (BERD) characteristics, by industry group based on the North American Industry Classification System (NAICS) | Variable: Wages and salaries, Mining and oil and gas extraction (x 1,000,000) | Units: $CAD, 1994-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-186.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Economic statistics; Official statistics; Population; Socioeconomic status; Work (physics); Descriptive statistics; Summary statistics; Executive summary","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001972006,0.002392779,0.002783684,0.008305699,0.003407039,0.00486051,0.005233896,0.00150052,0.09412058],"category_scores_gemma":[0.01861574,0.001642694,0.002081229,0.03979597,0.0005513908,0.002353278,0.002390665,0.002746836,0.06166954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04434345,"about_ca_system_score_gemma":0.1177375,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9931549,"about_ca_topic_score_gemma":0.9919403,"domain_scores_codex":[0.9962608,0.0002392514,0.0004483303,0.0005190241,0.001632486,0.0008999666],"domain_scores_gemma":[0.9675382,0.001209944,0.001071751,0.0009699184,0.02760706,0.001603019],"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":[0.00002172857,0.000005134545,0.0008638951,0.0002071875,0.00001700697,0.000004967998,0.00001864081,0.00007231253,0.00000628287,0.000270176,0.9972215,0.001291205],"study_design_scores_gemma":[0.0001798911,0.00001271575,0.02476251,0.0009571254,0.00007305556,0.00002294894,0.0005020209,0.0004067058,0.0001579367,0.0006367272,0.972201,0.00008736537],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004464131,0.00004243927,0.0000179289,0.00009810909,0.00002182231,0.00001210937,0.9990304,0.00004614918,0.0006864993],"genre_scores_gemma":[0.0006079843,0.0002334652,0.0002619511,0.000130382,0.00001737606,0.0001146859,0.9950321,0.00007978693,0.003522374],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9058794,"threshold_uncertainty_score":0.3217357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03010037759708563,"score_gpt":0.2422308373081229,"score_spread":0.2121304597110373,"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."}}