{"id":"W7027701452","doi":"","title":"DATA QUALITY AND LINKAGES: AN APPLICATION OF CRIME DATA IN THE CΓΓY OF LONDON, ONTARIO","year":2006,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Spurious relationship; Census; Data quality; Data set; Quality (philosophy); Crime analysis; Spatial analysis; Data collection","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":[0.00827141,0.0003044635,0.0004763104,0.004223383,0.00355852,0.00357263,0.001082542,0.0005569795,0.002025632],"category_scores_gemma":[0.06688785,0.0004642841,0.0005924987,0.02748055,0.001445908,0.001349337,0.002812019,0.0005237176,0.0001570369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03288576,"about_ca_system_score_gemma":0.03397039,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.973757,"about_ca_topic_score_gemma":0.9772334,"domain_scores_codex":[0.9911242,0.003599958,0.0005642078,0.000668386,0.003200588,0.0008428001],"domain_scores_gemma":[0.9620314,0.02188644,0.004430172,0.001950699,0.008881372,0.0008198646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001450132,0.0000555429,0.8865564,0.0004066926,0.0003021139,0.001225797,0.0239576,0.01088262,0.0002757218,0.009018402,0.006002445,0.0611717],"study_design_scores_gemma":[0.00003863901,0.0000593846,0.9297394,0.0002219352,0.0001274872,0.0002383662,0.0327662,0.01680888,0.0004152572,0.002025197,0.01749662,0.00006264383],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9698672,0.00105544,0.006413665,0.002571978,0.00002658363,0.0004833092,0.006863277,0.0001021657,0.01261651],"genre_scores_gemma":[0.9834324,0.0008717651,0.01071234,0.000056225,0.00001224241,0.0001758413,0.002429761,0.00004181592,0.002267794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03288576,"threshold_uncertainty_score":0.238604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2146867507083078,"score_gpt":0.3828287621179058,"score_spread":0.168142011409598,"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."}}