{"id":"W6901703169","doi":"10.6068/dp14ba8c453e017","title":"Trend 1986 - 2011. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Household, Family and Personal Income | Country: Canada | Table: Earnings of individuals, by selected characteristics and North American Industry Classification System (NAICS) | Variable: All age groups, Information, culture and recreation, Number of persons (number x 1,000) | Units: Constant 2011 $CAD, 1986-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-119.","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; Personal income; Economic statistics; Census; Socioeconomic status; Population; Total personal income; Official statistics; Demographic statistics; Summary 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":[0.001856322,0.002204369,0.002649314,0.007831802,0.002873868,0.004181104,0.004886115,0.00133185,0.07835329],"category_scores_gemma":[0.01637476,0.001553538,0.00186395,0.03745085,0.0005747771,0.002212307,0.002217816,0.002930759,0.05222593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04332455,"about_ca_system_score_gemma":0.0996621,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9912363,"about_ca_topic_score_gemma":0.9898113,"domain_scores_codex":[0.9966882,0.0002165444,0.000381287,0.0004723624,0.001497857,0.0007437096],"domain_scores_gemma":[0.9703598,0.001095716,0.0009694,0.0009329272,0.0253363,0.001305937],"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":[0.00002192738,0.000005793781,0.001010662,0.0002199132,0.00001841757,0.000005376082,0.00001782083,0.00009759167,0.000006990941,0.0003191934,0.9969054,0.001371024],"study_design_scores_gemma":[0.0001552952,0.00001115584,0.02762628,0.0008412862,0.00006128825,0.00002627158,0.0003994926,0.0004466859,0.0001684264,0.0005972326,0.9695857,0.00008094378],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005021685,0.00003737833,0.00001678171,0.00007437482,0.00001725625,0.00000973758,0.9991816,0.00004438621,0.0005682533],"genre_scores_gemma":[0.0005727012,0.0001794227,0.0002199929,0.00007586107,0.00001137261,0.00007992858,0.9961829,0.00005883753,0.002618968],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07835329,"threshold_uncertainty_score":0.3143431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0250226928669876,"score_gpt":0.2417207557873618,"score_spread":0.2166980629203742,"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."}}