{"id":"W6957789855","doi":"10.6068/dp14ba8f760ae60","title":"Trend 2006 - 2012. Statistics Canada. CANSIM: Children and Youth - Crime and Justice | 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 all cases, Arrears at beginning of year and arrears have remained constant, Total cases, length of enrolment, Spouse and children as beneficiaries | Units: #, 2006-2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-025.","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; Official statistics; Census; Economic Justice; Population; Law enforcement; Economic statistics; Alimony; Crime statistics; Demographic 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":[0.002245326,0.002420598,0.002943381,0.009249764,0.00336074,0.004546045,0.005371674,0.001389653,0.08575385],"category_scores_gemma":[0.01867129,0.002086447,0.002261182,0.04016245,0.0005990755,0.002343239,0.002348048,0.002960223,0.04024939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06378713,"about_ca_system_score_gemma":0.1579541,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9963294,"about_ca_topic_score_gemma":0.9948743,"domain_scores_codex":[0.9954576,0.000281495,0.0005723512,0.0005495677,0.002063906,0.001075101],"domain_scores_gemma":[0.961357,0.001238041,0.001363771,0.0009294879,0.03292035,0.002191404],"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":[0.00003205514,0.000009967783,0.00170098,0.0002678953,0.00002454457,0.000006808675,0.00003061312,0.0000986437,0.000008257273,0.0003601938,0.9956599,0.001800221],"study_design_scores_gemma":[0.0002748759,0.00002679496,0.05469039,0.001406919,0.0001174285,0.00004135806,0.0009611091,0.0007085949,0.0002339581,0.0006777134,0.9407448,0.0001162376],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009610059,0.00005757071,0.00002406358,0.0001450304,0.00003182936,0.00002408771,0.9984718,0.00006321428,0.001086237],"genre_scores_gemma":[0.001412653,0.0003962518,0.000422677,0.0002175669,0.00002578264,0.0001827892,0.9917413,0.0001174046,0.005483594],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08575385,"threshold_uncertainty_score":0.4628102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03973152659459066,"score_gpt":0.2682287738033259,"score_spread":0.2284972472087353,"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."}}