{"id":"W6939124136","doi":"10.6068/dp14ba8f03d0a54","title":"Trend 1997 - 2009. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Household Spending and Savings | Country: Canada | Table: Survey of household spending (SHS), household spending on recreation | Variable: Other recreational services (for example, fishing and hunting licenses, party planning), Estimated number of households reporting | Units: , 1997-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-120.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Recreation; Census; Descriptive statistics; Economic statistics; Household income; Consumer Expenditure Survey; Official statistics; Socioeconomic status; Population; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.007433089,0.001864059,0.003231672,0.0006025082,0.0008031279,0.0009522978,0.001736394,0.0009523122,0.0005628411],"category_scores_gemma":[0.001953191,0.001975733,0.000001420118,0.001361785,0.0004523873,0.001368438,0.001421298,0.001589166,0.000004766525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007857098,"about_ca_system_score_gemma":0.005582158,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9993191,"about_ca_topic_score_gemma":0.9935299,"domain_scores_codex":[0.9869529,0.001163102,0.004451135,0.003008974,0.002504685,0.001919201],"domain_scores_gemma":[0.9803429,0.005836709,0.009760851,0.002753784,0.000180876,0.001124842],"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":[0.0005307886,0.0001134041,0.05533789,0.002850748,0.0006240493,0.0004087841,0.0000214312,0.0004108385,0.0000907217,0.0004246173,0.9391235,0.00006325691],"study_design_scores_gemma":[0.002095565,0.000144682,0.005657361,0.002572829,0.00108346,0.0008631878,0.0004953259,0.007107059,0.000002229192,0.000001022545,0.978064,0.001913303],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002146375,0.001825482,0.00001815928,0.000001394073,0.0005810136,0.001717511,0.9924464,0.0003138654,0.0009498641],"genre_scores_gemma":[0.005537148,0.001053418,0.003686707,0.00008715838,0.0003130063,0.00002687888,0.9876202,0.001319418,0.0003560974],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04968053,"threshold_uncertainty_score":0.9994104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1017184390971416,"score_gpt":0.3074172765991593,"score_spread":0.2056988375020177,"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."}}