{"id":"W6920609947","doi":"10.6068/dp14ba8e1fe0a46","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: 55 years and over, Primary or secondary education, Hours worked, Paid workers, Chemical manufacturing, Females | 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; Wages and salaries; Economic statistics; Summary statistics; Socioeconomic status; Official statistics; Immigration; Statistical analysis","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":[0.0007566782,0.001060226,0.001491837,0.0002100107,0.0001896037,0.000518951,0.001018764,0.0008253037,0.0006542029],"category_scores_gemma":[0.0001695676,0.001032115,3.870074e-7,0.0007346898,0.001213797,0.0004316293,0.0005676553,0.001808926,0.000007904141],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004916642,"about_ca_system_score_gemma":0.01486786,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9967488,"about_ca_topic_score_gemma":0.9843618,"domain_scores_codex":[0.9945458,0.0006945544,0.00108324,0.001616615,0.001180851,0.0008789175],"domain_scores_gemma":[0.9940852,0.0009413196,0.001969946,0.001929186,0.0001615882,0.0009127376],"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":[0.0005895096,0.00008768293,0.02821314,0.00093775,0.0005215634,0.00006578789,0.00001825831,0.00000402529,0.0000114672,0.00005131658,0.9684729,0.001026629],"study_design_scores_gemma":[0.001159189,0.00006816123,0.01948364,0.0003739003,0.0006839844,0.0001793034,0.002236638,0.0001085249,1.861857e-7,6.612424e-8,0.9746093,0.00109715],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003559188,0.006476583,0.000001481754,0.000005010463,0.0004735318,0.001006404,0.9881241,0.0001242454,0.0002294139],"genre_scores_gemma":[0.001784557,0.001107223,0.0004784059,0.0002585736,0.000214952,0.00003523933,0.9935809,0.0005325125,0.002007643],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0143762,"threshold_uncertainty_score":0.9992129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02465694058225552,"score_gpt":0.2432746301472798,"score_spread":0.2186176895650243,"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."}}