{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00165211,0.002447712,0.002554697,0.007484465,0.002676849,0.004121738,0.005131085,0.001442845,0.07648992],"category_scores_gemma":[0.01357243,0.001539124,0.001851472,0.03541227,0.0005813794,0.002075974,0.001886314,0.003024126,0.05697767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04011789,"about_ca_system_score_gemma":0.08846958,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9917421,"about_ca_topic_score_gemma":0.9903193,"domain_scores_codex":[0.9967362,0.0001904952,0.0003095689,0.0004839603,0.001472074,0.0008077],"domain_scores_gemma":[0.974852,0.0009311145,0.0009647268,0.0007658898,0.02132001,0.001166377],"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":[0.00002528819,0.00000739936,0.001166109,0.0001830136,0.00001944596,0.000005520159,0.00001511453,0.0001181779,0.000009049439,0.0002374972,0.9969248,0.001288591],"study_design_scores_gemma":[0.0002123633,0.00001410166,0.03096316,0.0007589487,0.00006259798,0.00002509407,0.0004045145,0.0005515888,0.000221614,0.0005747835,0.9661235,0.00008777781],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005698552,0.00003893078,0.00001568799,0.00007566791,0.00002053307,0.000008471812,0.9991977,0.00004856597,0.0005374367],"genre_scores_gemma":[0.0005971612,0.0001527026,0.0001881832,0.00008573041,0.00001396774,0.00006083778,0.9961743,0.00006156149,0.002665468],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07648992,"threshold_uncertainty_score":0.291077,"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."}}