{"id":"W6957666602","doi":"10.6068/dp14ba8f45cf733","title":"Trend 2006 - 2012. Statistics Canada. CANSIM: Crime and Justice - Civil Courts and Family Law | Country: Canada | Table: Survey of Maintenance Enforcement Programs (SMEP), cases enrolled for the entire fiscal year, by activity status, type of beneficiary, change in arrears, length of enrolment | Variable: Total active cases, Arrears at beginning of year and no current arrears, Total cases, length of enrolment, Spouse as the only beneficiary | 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; Alimony; Law enforcement; Economic statistics; Enforcement; Economic Justice; Social statistics; Descriptive statistics; Official 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":[0.002332375,0.002449832,0.002896257,0.009247335,0.003314913,0.004829291,0.005375247,0.001491233,0.08153622],"category_scores_gemma":[0.02024201,0.001987907,0.002146885,0.03890667,0.0006195295,0.002423246,0.002481503,0.003190303,0.04031822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06041512,"about_ca_system_score_gemma":0.148255,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9959008,"about_ca_topic_score_gemma":0.9948645,"domain_scores_codex":[0.9955354,0.0002963848,0.0005330096,0.0005509233,0.002026579,0.001057765],"domain_scores_gemma":[0.9610187,0.001351332,0.001382958,0.001000573,0.03309054,0.002155884],"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":[0.00002625191,0.000008640267,0.001494176,0.0002530805,0.00002296873,0.000006780322,0.00002786831,0.0001015377,0.000007581329,0.0003404625,0.9960573,0.001653357],"study_design_scores_gemma":[0.0002391525,0.00002174533,0.04937494,0.001361907,0.0001107903,0.00003825155,0.0008490598,0.0006927297,0.0002061926,0.0007680553,0.9462224,0.0001147984],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007964203,0.00005862385,0.00002314795,0.0001454293,0.00003021559,0.00001952529,0.9987493,0.00005527664,0.0008389062],"genre_scores_gemma":[0.001201717,0.0003657354,0.0003949338,0.0002043092,0.00002420037,0.0001593035,0.9929187,0.0001016285,0.004629496],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08153622,"threshold_uncertainty_score":0.4383444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04637825882768717,"score_gpt":0.2824735607861018,"score_spread":0.2360953019584146,"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."}}