{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006778033,0.0008118812,0.001267592,0.000223286,0.0002864303,0.0003542686,0.0009942666,0.0005888348,0.000654846],"category_scores_gemma":[0.000237454,0.0008274,2.507637e-7,0.001467139,0.001057504,0.0003686855,0.0004590987,0.001065853,0.00001779879],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005039632,"about_ca_system_score_gemma":0.009887795,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9988927,"about_ca_topic_score_gemma":0.9985068,"domain_scores_codex":[0.99513,0.0009209464,0.0008720917,0.001310819,0.001073588,0.0006925625],"domain_scores_gemma":[0.9945492,0.000850317,0.001740927,0.001694271,0.000395671,0.0007695952],"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":[0.0005818597,0.00004173503,0.04057236,0.0004713891,0.00048326,0.00008973313,0.00001856983,0.00003203046,0.000007581623,0.0008732249,0.9566687,0.0001595059],"study_design_scores_gemma":[0.001062942,0.00009327996,0.01236325,0.0001862682,0.0005376239,0.0001198258,0.005374945,0.0004438826,3.524673e-8,4.498233e-8,0.9789442,0.0008737398],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008683876,0.00108259,0.00001053671,0.00000892427,0.0004168496,0.0009382875,0.9964067,0.0001257842,0.0001419272],"genre_scores_gemma":[0.001386915,0.0004436462,0.001102524,0.0001311842,0.0001181047,0.000007208509,0.9936588,0.0003041368,0.002847439],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02820912,"threshold_uncertainty_score":0.9994177,"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."}}