{"id":"W6976912567","doi":"10.6068/dp14ba8839ba224","title":"Trend 1994 - 2008. Statistics Canada. CANSIM: Environment - Natural Resources | Country: Canada | Table: Principal statistics of mineral industries, by North American Industry Classification System (NAICS) | Variable: Production workers' wages, Shale, clay and refractory mineral mining and quarrying | Units: , 1994-2008. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-086.","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; Mineral resource classification; Official statistics; Natural resource; Census; Summary statistics; Descriptive statistics; Production (economics)","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":[0.002055992,0.002293276,0.002601696,0.009782573,0.003410407,0.005001371,0.00475254,0.001440749,0.1014744],"category_scores_gemma":[0.01864257,0.001875354,0.001869544,0.04740051,0.000647389,0.002887385,0.002388253,0.003098797,0.06344933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05666523,"about_ca_system_score_gemma":0.1597861,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9948457,"about_ca_topic_score_gemma":0.9926953,"domain_scores_codex":[0.9952813,0.0002769198,0.0004938263,0.0005826728,0.002259592,0.001105706],"domain_scores_gemma":[0.9595199,0.001367647,0.001089595,0.001097277,0.03521009,0.001715514],"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":[0.0000167626,0.000004904884,0.0008423929,0.0001803014,0.00001599412,0.00000521519,0.00001785417,0.0000913941,0.00000760089,0.0003656465,0.9970286,0.001423208],"study_design_scores_gemma":[0.0001144327,0.00000960781,0.02035699,0.000683559,0.00005692909,0.00002166843,0.0004238039,0.0004173067,0.0001562378,0.0006536879,0.9770274,0.00007824362],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005174479,0.00004638736,0.00002813141,0.0001342926,0.00002931552,0.0000154804,0.9984046,0.00007253682,0.001217509],"genre_scores_gemma":[0.000917457,0.0003140865,0.000488259,0.0001760275,0.00001934143,0.0001405103,0.992195,0.0001584132,0.005590928],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1014744,"threshold_uncertainty_score":0.4111369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02478107448947688,"score_gpt":0.2302909832979066,"score_spread":0.2055099088084298,"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."}}