{"id":"W7084108788","doi":"10.64628/aam.jkydhwskw","title":"Protecting children’s data privacy in the smart city","year":2019,"lang":"en","type":"article","venue":"","topic":"Brazilian Legal Issues","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Smart city; Information privacy; Confidentiality; Smart card; Field (mathematics); Privacy software","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00231404,0.00005275552,0.00007400653,0.00002402587,0.0002107371,0.000132529,0.001328191,0.00004773362,0.0008829851],"category_scores_gemma":[0.0006295535,0.00003521183,0.00001472895,0.0002610052,0.0000723634,0.0004725424,0.0002172661,0.0002367421,0.0003543167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002837207,"about_ca_system_score_gemma":0.00006710883,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03621451,"about_ca_topic_score_gemma":0.01768816,"domain_scores_codex":[0.9988504,0.0003129908,0.0001029842,0.0002247756,0.0002876287,0.0002212174],"domain_scores_gemma":[0.9991634,0.0001264327,0.00003291025,0.0006365634,0.00001459819,0.00002609177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002784245,0.00003791334,0.9594522,0.000002760657,0.000004381439,8.513083e-7,0.02562761,4.417838e-7,0.00002079241,0.008299864,0.002336735,0.004213716],"study_design_scores_gemma":[0.0002373288,0.000021797,0.8452767,0.00002158439,0.000004065517,0.000001358838,0.009052747,0.00009716588,0.00009340994,0.001546023,0.1434676,0.0001801838],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.825083,0.00002579803,0.0000114014,0.005989384,0.00009545353,0.0007040299,0.000002009493,0.00005556289,0.1680333],"genre_scores_gemma":[0.9964218,0.000003196073,0.0003356647,0.0003578156,0.0001467244,0.000007328503,0.000004378961,0.000004053586,0.002719064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1713387,"threshold_uncertainty_score":0.9870411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06744085905036096,"score_gpt":0.3566485921758581,"score_spread":0.2892077331254972,"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."}}