{"id":"W6920219749","doi":"10.6068/dp14ba8c5a37557","title":"Trend 2007 - 2012. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Labour statistics consistent with the System of National Accounts (SNA), by province and territory, job category and North American Industry Classification System (NAICS) | Variable: Agencies, brokerages and other insurance related activities, Hours worked for employee jobs | Units: Hours x 1,000, 2007-2012. 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; Official statistics; Summary statistics; National accounts; Socioeconomic status; Statistics education; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001909034,0.002611145,0.002883644,0.008590902,0.002992422,0.004908791,0.005290736,0.001536829,0.08302562],"category_scores_gemma":[0.01690094,0.001845185,0.001967312,0.04376107,0.0006178863,0.002480822,0.002048794,0.003340283,0.05592487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05246215,"about_ca_system_score_gemma":0.1242242,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.994109,"about_ca_topic_score_gemma":0.9921898,"domain_scores_codex":[0.9957046,0.0002598866,0.000420177,0.0005676763,0.002027445,0.001020177],"domain_scores_gemma":[0.9651487,0.001180432,0.001150032,0.0009196491,0.03015935,0.001441731],"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":[0.0000227695,0.00000707589,0.0009268546,0.0001754649,0.00001837833,0.000005458529,0.00001548248,0.0001056586,0.000007354057,0.0002845766,0.997218,0.001212934],"study_design_scores_gemma":[0.0001944888,0.00001346318,0.02870155,0.0008062711,0.00006684999,0.00002537954,0.0004454663,0.0006076171,0.0002168811,0.000652691,0.9681743,0.00009508525],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005407185,0.00004040463,0.0000170319,0.00009463995,0.00002312297,0.00001035544,0.9990636,0.00005148111,0.0006454136],"genre_scores_gemma":[0.000705376,0.0001975907,0.0002587747,0.0001178762,0.00001673194,0.00008423672,0.9951113,0.00008736832,0.003420749],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08302562,"threshold_uncertainty_score":0.3806413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02072545086938857,"score_gpt":0.2305440206581834,"score_spread":0.2098185697887948,"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."}}