{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001240848,0.001127948,0.00149867,0.0001663276,0.0004419696,0.0005914395,0.001081972,0.00057614,0.0001206852],"category_scores_gemma":[0.0001694244,0.0009370577,3.645961e-7,0.0004714607,0.001890569,0.0005768522,0.0003520224,0.001310454,0.000002335831],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007986617,"about_ca_system_score_gemma":0.01788919,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9971093,"about_ca_topic_score_gemma":0.9964293,"domain_scores_codex":[0.9938403,0.0008912571,0.001116951,0.00149818,0.001697271,0.0009560302],"domain_scores_gemma":[0.9931072,0.001779803,0.002512846,0.00159346,0.0003887761,0.0006178892],"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":[0.0004248864,0.00004489894,0.01557644,0.00221874,0.0009334089,0.0000852029,0.00002350689,0.00002590233,0.000005159278,0.001202847,0.9793916,0.00006735908],"study_design_scores_gemma":[0.001528964,0.0001606931,0.002646022,0.0003000835,0.0006850609,0.0003327082,0.004281682,0.0009753314,4.423625e-8,8.080613e-8,0.9880225,0.001066819],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002508078,0.007726928,0.00002395957,0.000004524019,0.0003020271,0.001811033,0.9894148,0.0001209922,0.0003448964],"genre_scores_gemma":[0.001833513,0.0004795937,0.0003457222,0.0001963802,0.0001502132,0.0001045586,0.9934112,0.0005698304,0.002909012],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01709052,"threshold_uncertainty_score":0.999308,"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."}}