{"id":"W2580276317","doi":"10.1093/tandt/ttw250","title":"Using Social Insurance numbers for identification purposes: Canadian perspective on legal and privacy risks","year":2016,"lang":"en","type":"article","venue":"Trusts & Trustees","topic":"Criminal Law and Evidence","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Circulation (fluid dynamics); Business; Perspective (graphical); Social insurance; Privacy policy; Information privacy; Actuarial science; Finance; Internet privacy; Law; Political science","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":[0.02685906,0.000748189,0.0009195835,0.008019152,0.01942399,0.02149627,0.004451857,0.01394099,0.005903088],"category_scores_gemma":[0.0678971,0.0008274469,0.001134595,0.008812496,0.04285331,0.01318896,0.007131264,0.01349042,0.0006549987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09834824,"about_ca_system_score_gemma":0.1466224,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.950614,"about_ca_topic_score_gemma":0.9411034,"domain_scores_codex":[0.9556055,0.01204673,0.001792417,0.002806291,0.0226426,0.005106501],"domain_scores_gemma":[0.9160313,0.04148013,0.004377168,0.003674589,0.03022977,0.004207064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001308366,0.000009983447,0.001276289,0.00008633533,0.00001143379,0.0003961477,0.01031659,0.0002456261,0.0000808093,0.9614152,0.01346962,0.01267893],"study_design_scores_gemma":[0.00002412615,0.00003788918,0.006716063,0.002866594,0.00009173612,0.00118161,0.03016677,0.001809832,0.0006351265,0.3419246,0.6141811,0.0003645736],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02022987,0.02528669,0.013328,0.4769545,0.001355566,0.0001522143,0.0006455967,0.00005834903,0.461989],"genre_scores_gemma":[0.8183843,0.05088373,0.01435096,0.06940963,0.001134103,0.0001191442,0.0003363469,0.0001179862,0.04526379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09834824,"threshold_uncertainty_score":0.7135698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.127247274481775,"score_gpt":0.4177535648375083,"score_spread":0.2905062903557333,"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."}}