{"id":"W1978459025","doi":"10.1109/noms.2014.6838325","title":"A case study for a secure and robust geo-fencing and access control framework","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Robustness (evolution); Fencing; Computer science; Access control; Computer security; Software; Focus (optics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002227456,0.0005794968,0.0004686965,0.0007304292,0.002718073,0.002434586,0.001588953,0.004328301,0.004274117],"category_scores_gemma":[0.003662859,0.0002593154,0.0007365928,0.0005594555,0.00226975,0.002485821,0.00298168,0.00155209,0.0009855438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001540827,"about_ca_system_score_gemma":0.001515503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009852136,"about_ca_topic_score_gemma":0.008326327,"domain_scores_codex":[0.9967663,0.001211456,0.000167687,0.0003445431,0.0009195483,0.0005904149],"domain_scores_gemma":[0.9972938,0.000964932,0.0002496368,0.0005618892,0.0004081666,0.0005215721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001273685,0.001718891,0.03483802,0.0007023758,0.0001789249,0.09461577,0.01343786,0.1983859,0.0500753,0.4554789,0.01743159,0.1318628],"study_design_scores_gemma":[0.0004184428,0.002574413,0.01240034,0.0003747451,0.0001885658,0.04121211,0.01808509,0.5596585,0.05493045,0.06400885,0.2458216,0.0003269875],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4292181,0.0006003631,0.5069299,0.005102975,0.0001939833,0.001291847,0.0003653007,0.001203983,0.05509352],"genre_scores_gemma":[0.868865,0.0002246458,0.1179905,0.0002227928,0.00003395618,0.0002711436,0.000135154,0.00008230876,0.01217449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009852136,"threshold_uncertainty_score":0.0195896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174916007503317,"score_gpt":0.2473709889896586,"score_spread":0.2298793882393269,"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."}}