{"id":"W6976948271","doi":"10.6068/dp14ba8d6b8b560","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, University degrees or above, Labour compensation, Paid workers, Construction, Females | Units: , 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; Wages and salaries; Economic statistics; Socioeconomic status; Summary statistics; 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":[0.001714322,0.002369547,0.002601028,0.007913669,0.002955204,0.004456006,0.005112831,0.0015465,0.08660932],"category_scores_gemma":[0.01582081,0.001585999,0.001766367,0.03793149,0.0006282057,0.00220406,0.002109028,0.003071079,0.06235879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04080897,"about_ca_system_score_gemma":0.09717616,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.991833,"about_ca_topic_score_gemma":0.9902433,"domain_scores_codex":[0.9965042,0.000222459,0.0003473868,0.0005205572,0.001520933,0.0008845674],"domain_scores_gemma":[0.9725574,0.001149536,0.001023853,0.0009042651,0.02294459,0.001420343],"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":[0.00002035678,0.00000618766,0.0008438443,0.0001608803,0.00001577083,0.000005203004,0.00001610179,0.00009409532,0.00000752432,0.0002621312,0.9974616,0.00110639],"study_design_scores_gemma":[0.0001800237,0.00001177187,0.02332457,0.0007463376,0.00005473766,0.00002472394,0.0004256374,0.0004573664,0.0001937477,0.0006572426,0.9738383,0.00008559549],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004607152,0.00003412545,0.00001671704,0.00007896395,0.00001798693,0.000008886597,0.9992106,0.0000494544,0.000537226],"genre_scores_gemma":[0.0005338438,0.0001544388,0.0002067201,0.00008714797,0.00001247018,0.00006985015,0.9962727,0.00007078239,0.002592019],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08660932,"threshold_uncertainty_score":0.2960912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03444175153610591,"score_gpt":0.2377631720982416,"score_spread":0.2033214205621357,"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."}}