{"id":"W6939208276","doi":"10.6068/dp14ba8aeda8825","title":"Trend 1997 - 2013. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Labour force survey estimates (LFS), wages of employees by type of work, North American Industry Classification System (NAICS), sex and age group | Variable: 25 to 54 years, Construction, Females, Total employees, Average weekly wage rate | Units: Current $CAD, 1997-2013. 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; Wage; Summary statistics; Socioeconomic status; Official statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001291141,0.001225936,0.002121411,0.0002680267,0.0001821996,0.0004052331,0.00156517,0.0006058898,0.0003314035],"category_scores_gemma":[0.0004028814,0.001240433,5.02949e-7,0.001464469,0.001199229,0.000375057,0.0008178455,0.001447954,0.0000197993],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003864183,"about_ca_system_score_gemma":0.0066808,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9992996,"about_ca_topic_score_gemma":0.998453,"domain_scores_codex":[0.9928336,0.00136416,0.001616336,0.001735638,0.001390481,0.001059806],"domain_scores_gemma":[0.9926016,0.001267651,0.002337617,0.002573014,0.00030893,0.0009111257],"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":[0.0002872676,0.0001104159,0.02033532,0.001679782,0.0006362794,0.0001306345,0.00001777316,0.00003958926,0.00001182213,0.0003296274,0.9762864,0.0001351101],"study_design_scores_gemma":[0.0008457769,0.0002356079,0.005107976,0.000289059,0.0005261349,0.00008864565,0.0005493357,0.0002435185,1.523221e-7,8.031849e-8,0.9908963,0.001217384],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007773188,0.003975151,0.00001273298,0.000002472956,0.0005523856,0.001072151,0.9933975,0.000149823,0.00006046714],"genre_scores_gemma":[0.001863747,0.0009530197,0.0004726692,0.00004879263,0.0001251373,0.00003137034,0.9947339,0.0005809527,0.001190384],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01522735,"threshold_uncertainty_score":0.9990045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03174354721394047,"score_gpt":0.25883032178978,"score_spread":0.2270867745758396,"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."}}