{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004524664,0.0001206517,0.0001551349,0.00008968592,0.001212806,0.0001817961,0.0002116704,0.0001072107,0.00006444383],"category_scores_gemma":[0.0004629928,0.0001009293,0.00006578706,0.0001166348,0.0003732983,0.0005712427,0.00001647713,0.00006794065,0.00002150238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007166543,"about_ca_system_score_gemma":0.0004253364,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.39173,"about_ca_topic_score_gemma":0.3635773,"domain_scores_codex":[0.9987556,0.0001062274,0.0001682402,0.0003254718,0.0002458485,0.0003986106],"domain_scores_gemma":[0.9992382,0.000186207,0.0001004246,0.0001205282,0.0001744306,0.0001801926],"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":[0.0003160877,0.00008071389,0.02461751,0.00005922362,0.00007093469,0.00001337877,0.07905818,0.000006869295,0.001449936,0.8170186,0.003918237,0.07339031],"study_design_scores_gemma":[0.002179334,0.0002905012,0.3682292,0.0004363314,0.000244633,0.00001621706,0.08718204,0.0001011418,0.00197055,0.06480591,0.4730274,0.001516685],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729179,0.000146769,0.0001846982,0.01332633,0.0003961,0.0005940426,0.000117231,0.00005520408,0.0122618],"genre_scores_gemma":[0.997986,0.00006629843,0.0001729257,0.0001395052,0.0004334942,0.00002931342,0.000001286566,0.00001430868,0.001156914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7522127,"threshold_uncertainty_score":0.9328049,"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."}}