{"id":"W2013328030","doi":"10.1007/s10207-009-0088-z","title":"Uncertain inference control in privacy protection","year":2009,"lang":"en","type":"article","venue":"International Journal of Information Security","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint Mary's University; York University","funders":"","keywords":"Context (archaeology); Computer science; Inference; Enabling; Key (lock); Control (management); Computer security; Cryptography; Context awareness; Information privacy; Context management; Risk analysis (engineering); Data science; Ubiquitous computing; Human–computer interaction; Artificial intelligence; Business","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":[0.0009202496,0.00009883487,0.000175168,0.0006192521,0.00002955654,0.0003304774,0.0008675478,0.00006435435,0.00001886719],"category_scores_gemma":[0.0004971459,0.00009174366,0.00008699314,0.0002833645,0.00001398146,0.00692951,0.00004581133,0.0002935209,0.00004376897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002496903,"about_ca_system_score_gemma":0.0001810089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004010063,"about_ca_topic_score_gemma":0.000009072113,"domain_scores_codex":[0.9980782,0.0001065232,0.000911978,0.00006593974,0.0007142821,0.0001230805],"domain_scores_gemma":[0.9975546,0.0001027405,0.0008954324,0.0001390583,0.001242694,0.00006546515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004291916,0.0004831619,0.004781242,0.00002527758,0.0001285103,0.00006876702,0.02104376,0.001936766,0.001560965,0.03916974,0.0012706,0.929102],"study_design_scores_gemma":[0.02092848,0.001608272,0.2116999,0.001309264,0.00002504037,0.003186613,0.001570146,0.4992665,0.007657582,0.1078,0.1435535,0.001394698],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09777985,0.00002372477,0.8941976,0.005606966,0.0008905594,0.000215492,0.000006033496,0.00003772753,0.001242046],"genre_scores_gemma":[0.9974726,0.00001290753,0.001269682,0.00111805,0.000114261,0.000005015997,0.000002434031,0.00000139151,0.000003656448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9277073,"threshold_uncertainty_score":0.5023727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01899064050931341,"score_gpt":0.2893987264470002,"score_spread":0.2704080859376867,"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."}}