{"id":"W6920252354","doi":"10.6068/dp14ba9049fa179","title":"Trend 2006 - 2012. Statistics Canada. CANSIM: Crime and Justice - Civil Courts and Family Law | Country: Canada | Table: Survey of Maintenance Enforcement Programs (SMEP), enrolled cases, by activity status, compliance with regular and total payments, arrears status and assignment status at March 31 | Variable: Total active cases, Unknown arrears owing at beginning of March of the fiscal year, Non compliance with payment in March of the fiscal year, Total number of cases, Non-assigned payment, Regular payment due | Units: #, 2006-2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-038.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Arrears; Payment; Enforcement; Law enforcement; Economic statistics; Economic Justice; Alimony; Census","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":[0.002237139,0.0023247,0.002926276,0.009084484,0.003259729,0.004605948,0.005395969,0.001418814,0.08370514],"category_scores_gemma":[0.02041877,0.001917744,0.002066157,0.03859385,0.0006200612,0.002326624,0.002412573,0.003229983,0.03917382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05851487,"about_ca_system_score_gemma":0.1459233,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9953598,"about_ca_topic_score_gemma":0.9943151,"domain_scores_codex":[0.9955914,0.0002959267,0.0005799484,0.0005569549,0.001945739,0.001030087],"domain_scores_gemma":[0.9604466,0.001445392,0.001556809,0.001001313,0.03326022,0.002289692],"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":[0.00002832735,0.000009493262,0.001618309,0.0002712689,0.00002378394,0.000006580495,0.0000280098,0.00009026673,0.000007441142,0.0002967855,0.996103,0.001516652],"study_design_scores_gemma":[0.0003074126,0.00002695131,0.06210364,0.001697339,0.0001334949,0.0000430015,0.001028669,0.0007356464,0.0002261692,0.0008296581,0.9327427,0.0001254671],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000860869,0.00004939149,0.00002119186,0.000124672,0.00002601485,0.00002075264,0.9988744,0.0000500948,0.0007474287],"genre_scores_gemma":[0.001200857,0.0003141231,0.0003569482,0.0001858344,0.00002267068,0.0001659542,0.993733,0.0000884933,0.003932188],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08370514,"threshold_uncertainty_score":0.424557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02794763692712618,"score_gpt":0.2597551061177275,"score_spread":0.2318074691906013,"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."}}