{"id":"W6957721521","doi":"10.6068/dp14ba903c55992","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, Air, rail, water and scenic and sightseeing transportation and support activities for transportation, 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; Official statistics; Immigration; Socioeconomic status; Compensation (psychology)","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":[0.001546486,0.002481414,0.002618275,0.007595443,0.002711468,0.003989492,0.004971806,0.001347435,0.07437848],"category_scores_gemma":[0.01237611,0.001577132,0.001817675,0.03896204,0.0005629277,0.002039414,0.001899344,0.003007234,0.05195617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04253466,"about_ca_system_score_gemma":0.09350255,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9935912,"about_ca_topic_score_gemma":0.9920358,"domain_scores_codex":[0.996687,0.0001734317,0.0003373588,0.0004621284,0.00150745,0.0008327872],"domain_scores_gemma":[0.9744537,0.0008004956,0.0009498078,0.0006321397,0.02201514,0.001148755],"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":[0.0000292114,0.000009637621,0.001513247,0.000220512,0.00002237611,0.000006651002,0.00001957778,0.000128473,0.000009401186,0.0002567993,0.9962562,0.00152772],"study_design_scores_gemma":[0.000229034,0.00001997301,0.05024271,0.0008853061,0.00007682315,0.00003044684,0.0005916107,0.0006516909,0.0002514247,0.0005674622,0.9463534,0.0001001681],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007404002,0.00004275491,0.00001576215,0.00007902157,0.00002220898,0.000009747439,0.9991265,0.0000445201,0.0005854811],"genre_scores_gemma":[0.0007648491,0.0001848791,0.000174644,0.00009223378,0.00001506958,0.00006612454,0.9953051,0.00005745114,0.003339658],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07437848,"threshold_uncertainty_score":0.308612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02366811747348381,"score_gpt":0.2396369395005984,"score_spread":0.2159688220271146,"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."}}