{"id":"W6920273093","doi":"10.6068/dp14ba8d429ee94","title":"Trend 1986 - 2011. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Earnings of individuals, by selected characteristics and National Occupational Classification | Variable: 15 to 24 years, Trades, transport and equipment operators and related occupations, Median earnings | Units: # Persons $CAD, 1986-2011. 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; Census; Economic statistics; Socioeconomic status; Summary statistics; Official statistics; Wages and salaries; Immigration; Personal income","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":[0.001829511,0.002377487,0.002549594,0.008561836,0.00316671,0.004592599,0.004899048,0.001417643,0.07832985],"category_scores_gemma":[0.01429912,0.001638235,0.001807417,0.04020118,0.000580995,0.002248216,0.002031398,0.002992873,0.04972926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04796614,"about_ca_system_score_gemma":0.1154756,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9948231,"about_ca_topic_score_gemma":0.9932408,"domain_scores_codex":[0.9960352,0.0002339023,0.0003864714,0.0004890293,0.001851136,0.00100435],"domain_scores_gemma":[0.9727674,0.0009059844,0.0009641032,0.0007211547,0.0233486,0.001292864],"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":[0.00002436574,0.000007832545,0.001170714,0.0002120752,0.00001947901,0.000007002578,0.00002080762,0.0001179811,0.000007902593,0.0003719579,0.9964036,0.001636356],"study_design_scores_gemma":[0.0001540117,0.00001357895,0.03206221,0.000840621,0.00006203989,0.00002762318,0.0004924908,0.0005408288,0.0001941354,0.0006098527,0.9649112,0.00009134052],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006637836,0.00005353745,0.00002200689,0.0001060152,0.00002248526,0.00001252795,0.9988207,0.00005001402,0.0008462555],"genre_scores_gemma":[0.0009379452,0.0002943985,0.0003062594,0.0001193906,0.0000167891,0.00008891154,0.9938705,0.00008080224,0.004285015],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07832985,"threshold_uncertainty_score":0.3480203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02341669748563415,"score_gpt":0.247202616145243,"score_spread":0.2237859186596088,"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."}}