{"id":"W6920521753","doi":"10.6068/dp14ba8cf443370","title":"Trend 1961 - 2010. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Hours worked and labour compensation by type of worker and North American Industry Classification System (NAICS) | Variable: 15 to 34 years, Primary or secondary education, Hours worked, Paid workers, Manufacturing, Males | Units: Hours x 1,000, 1961-2010. 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; Wages and salaries; Summary statistics; Socioeconomic status; Official statistics; Immigration; Compensation (psychology)","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":[0.001713211,0.002433,0.002630251,0.007691694,0.002844048,0.004307915,0.005182539,0.001389524,0.08114413],"category_scores_gemma":[0.01380173,0.001604573,0.001878971,0.03753172,0.0005934516,0.002179926,0.001917179,0.003096273,0.05826753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04546844,"about_ca_system_score_gemma":0.1049874,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9934164,"about_ca_topic_score_gemma":0.991617,"domain_scores_codex":[0.9962852,0.0002074196,0.0003464312,0.0005207841,0.001729126,0.0009109774],"domain_scores_gemma":[0.9719705,0.0009508804,0.0009982929,0.0007738193,0.02406134,0.00124515],"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":[0.00002426761,0.000007521483,0.001111023,0.0001803072,0.00001858762,0.000005600638,0.00001630839,0.0001107411,0.000008710773,0.0002696725,0.9968725,0.001374855],"study_design_scores_gemma":[0.000190117,0.00001396987,0.03024153,0.0007176706,0.00006000781,0.00002498377,0.0004303578,0.0005030227,0.0002170713,0.0005624657,0.9669521,0.00008680142],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006227774,0.00004536435,0.0000184546,0.00009445703,0.00002468763,0.000009837233,0.9989915,0.0000543915,0.0006989811],"genre_scores_gemma":[0.000722002,0.0002013464,0.0002376295,0.0001123207,0.00001681832,0.0000712677,0.9946473,0.0000801785,0.003911084],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08114413,"threshold_uncertainty_score":0.3298981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377612092434826,"score_gpt":0.2421231406544986,"score_spread":0.2183470197301504,"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."}}