{"id":"W6938962862","doi":"10.6068/dp14ba8a4f24e15","title":"Trend 1997 - 2013. Statistics Canada. CANSIM: Labor - Employment and Unemployment | Country: Canada | Table: Labour force survey estimates (LFS), employees by job permanency, North American Industry Classification System (NAICS), sex and age group | Variable: 55 to 64 years, Forestry, fishing, mining, quarrying, oil and gas, Temporary, Males | Units: # Persons x 1,000, 1997-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-136.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Unemployment; Census; Official statistics; Economic statistics; Summary statistics; Socioeconomic status; Wages and salaries; Private sector; Social statistics; Descriptive statistics","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":[0.00211755,0.002135769,0.00281319,0.007424165,0.0031722,0.004665938,0.005089059,0.001353509,0.08617369],"category_scores_gemma":[0.01601448,0.001811822,0.002170118,0.03665524,0.0005749213,0.00220653,0.002319219,0.003005442,0.0521385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05021525,"about_ca_system_score_gemma":0.1320743,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9959422,"about_ca_topic_score_gemma":0.9945392,"domain_scores_codex":[0.9958379,0.0002678931,0.0004513327,0.0005103563,0.001866588,0.001065818],"domain_scores_gemma":[0.9695796,0.001131609,0.0009586934,0.0008034479,0.0258223,0.001704474],"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":[0.0000306038,0.000007823428,0.001255657,0.0002440987,0.00002314158,0.000006448167,0.00002536769,0.00009584967,0.0000079637,0.0002782783,0.996317,0.001707602],"study_design_scores_gemma":[0.0002377543,0.00002016175,0.04125255,0.001118647,0.00009598494,0.0000331891,0.0007105589,0.0005975423,0.0002038912,0.0005923635,0.9550303,0.0001071374],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007927066,0.00005160946,0.00002404858,0.0001197928,0.00002936538,0.00001595039,0.9987426,0.00006222293,0.0008751375],"genre_scores_gemma":[0.001003049,0.0002881177,0.0003464563,0.0001807451,0.00001926121,0.0001320579,0.9926761,0.0001089396,0.005245212],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08617369,"threshold_uncertainty_score":0.3643389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03089137361927093,"score_gpt":0.257112708769293,"score_spread":0.2262213351500221,"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."}}