{"id":"W1973378347","doi":"10.1117/12.778008","title":"Using received signal strength variation for surveillance in residential areas","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Signal strength; Wireless sensor network; Computer science; Wireless; Received signal strength indication; Ranging; SIGNAL (programming language); Interference (communication); Computer security; Real-time computing; Wireless network; Computer network; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000428742,0.0002721713,0.0003811268,0.0001811026,0.00008997587,0.00005416838,0.0005777578,0.0002504371,0.000008577417],"category_scores_gemma":[0.0005098369,0.0002589586,0.000355086,0.0004224583,0.0001473661,0.0004420858,0.00006744414,0.0002436515,5.131495e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002947316,"about_ca_system_score_gemma":0.00004318558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001498915,"about_ca_topic_score_gemma":0.000001010683,"domain_scores_codex":[0.9982135,1.756446e-8,0.0006832818,0.000287421,0.0004359277,0.0003798594],"domain_scores_gemma":[0.998565,0.0001497406,0.000207204,0.00005065349,0.0009705093,0.00005686022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000207987,0.000136573,0.003876797,0.0009376804,0.0004699748,3.480694e-7,0.0008678122,0.02788413,0.7599415,0.2029988,0.002215146,0.0004632744],"study_design_scores_gemma":[0.0021825,0.000197067,0.008273251,0.0002919025,0.00007425134,0.00002377161,0.0008855551,0.7257943,0.2583263,0.002353153,0.001013439,0.0005845067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963825,0.00009797429,0.001556988,0.0002757958,0.0002726888,0.0005587229,0.00005825677,0.000216971,0.0005800455],"genre_scores_gemma":[0.9227464,0.0001337298,0.07657676,0.0000186958,0.0002736785,0.000118502,0.00001471519,0.00006502783,0.00005245091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6979102,"threshold_uncertainty_score":0.9999863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01658299843419566,"score_gpt":0.2289245424240156,"score_spread":0.2123415439898199,"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."}}