{"id":"W6976413679","doi":"10.6068/dp14ba88c00c331","title":"Trend 1997 - 2011. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Labour statistics by business sector industry and non-commercial activity, consistent with the System of National Accounts, by North American Industry Classification System (NAICS) | Variable: Total compensation per job, Ship and boat building, Business sector | Units: , 1997-2011. 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; Economic statistics; Census; Wages and salaries; Official statistics; Summary statistics; Business statistics; Statistics education; Socioeconomic status; Compensation of employees","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":[0.001897737,0.002461249,0.002722377,0.008201987,0.003143453,0.004852324,0.005071743,0.001506986,0.08219864],"category_scores_gemma":[0.01542673,0.001781182,0.001916918,0.03944473,0.0006296053,0.002461167,0.002196291,0.003268092,0.06086123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04750286,"about_ca_system_score_gemma":0.1202564,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9934615,"about_ca_topic_score_gemma":0.9916113,"domain_scores_codex":[0.9957941,0.0002547966,0.0004040453,0.0005414282,0.001988151,0.001017517],"domain_scores_gemma":[0.9678394,0.001032878,0.001024166,0.0009053738,0.02781245,0.001385659],"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":[0.00002139152,0.000006689211,0.0008648924,0.0001742148,0.00001707671,0.000006100484,0.0000159386,0.00009319776,0.000007351161,0.0002751225,0.9972531,0.001265017],"study_design_scores_gemma":[0.000159405,0.00001177526,0.02400672,0.0007249049,0.00005668432,0.00002643545,0.0004265068,0.0004519832,0.0001987037,0.0005744041,0.9732785,0.00008395506],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005487358,0.0000412503,0.00001827577,0.0001014852,0.00002494419,0.00001097093,0.9989647,0.00005175659,0.0007319242],"genre_scores_gemma":[0.0006419028,0.000195383,0.000247737,0.0001151712,0.00001615733,0.00007789557,0.99507,0.00008928667,0.003546578],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08219864,"threshold_uncertainty_score":0.344659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02485594224822282,"score_gpt":0.2360945014293758,"score_spread":0.2112385591811529,"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."}}