{"id":"W6920189813","doi":"10.6068/dp14ba8c37ec627","title":"Trend 1986 - 2011. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Earnings of individuals, by selected characteristics and North American Industry Classification System (NAICS) | Variable: All education levels, Accommodation and food services, Number of persons (number x 1,000) | Units: Constant 2011 $CAD, 1986-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-145.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Earnings; Census; Economic statistics; Socioeconomic status; Official statistics; Summary statistics; Wages and salaries; Publication; Immigration","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":[0.001780701,0.002276553,0.002498706,0.007989204,0.002855699,0.004349214,0.004826654,0.001422555,0.08202964],"category_scores_gemma":[0.0153251,0.001584506,0.001735088,0.03770354,0.0005850518,0.00216028,0.002026537,0.002926622,0.05612493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04321944,"about_ca_system_score_gemma":0.1012028,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9923409,"about_ca_topic_score_gemma":0.9908776,"domain_scores_codex":[0.9963436,0.0002360555,0.0003740914,0.0004929081,0.001672122,0.0008812608],"domain_scores_gemma":[0.9711728,0.001085208,0.001062991,0.0008586558,0.02443702,0.001383356],"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":[0.00002130495,0.0000067407,0.0009114697,0.0001740166,0.00001602666,0.000005389963,0.00001556853,0.0001042275,0.000006713997,0.0003022916,0.9971772,0.00125887],"study_design_scores_gemma":[0.0001692848,0.00001274947,0.02666535,0.0007669472,0.00005610752,0.00002562142,0.0004100198,0.0005307993,0.0001899891,0.0005993321,0.97049,0.00008377842],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005348003,0.00003940028,0.00001792782,0.00008783318,0.00001885747,0.00001001483,0.9990788,0.00004970456,0.0006439605],"genre_scores_gemma":[0.0006357844,0.0002002482,0.000231259,0.00009244563,0.00001370866,0.00007403745,0.9954908,0.00006970965,0.003191946],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08202964,"threshold_uncertainty_score":0.3135804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02491724378987077,"score_gpt":0.2476776039600803,"score_spread":0.2227603601702095,"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."}}