{"id":"W2003588349","doi":"10.1145/2658999","title":"Wireless Fingerprints Inside a Wireless Sensor Network","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Sensor Networks","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Wireless sensor network; Wireless; Wireless network; Key distribution in wireless sensor networks; Fingerprint (computing); Real-time computing; Wi-Fi array; Node (physics); Transmission (telecommunications); Sample (material); Computer network; Telecommunications; Artificial intelligence; Engineering","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":[0.0005617561,0.0004295548,0.0004354244,0.0002267589,0.0004269955,0.0002989299,0.001360976,0.0003393771,0.0000336734],"category_scores_gemma":[0.00005138976,0.0004471985,0.0001923625,0.001394677,0.000118653,0.0006622074,0.00005144515,0.0007213483,0.0003305882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002651653,"about_ca_system_score_gemma":0.0001471209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000494937,"about_ca_topic_score_gemma":0.00004822977,"domain_scores_codex":[0.9964985,0.0003712777,0.0006506176,0.000963554,0.0007336537,0.0007823958],"domain_scores_gemma":[0.9960558,0.0006364239,0.0002753442,0.002149362,0.0003919359,0.0004911502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007538813,0.0001854712,0.0003588466,0.000007787525,0.00007051841,0.0000325822,0.0003822125,0.8610142,0.0003168463,0.002596455,0.001069667,0.13389],"study_design_scores_gemma":[0.0009909285,0.0001472449,0.002392448,0.00010572,0.00003684502,0.00007450863,0.0001295188,0.9890853,0.001227858,0.001976337,0.003151163,0.0006821934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04959112,0.00005493324,0.9440852,0.002607691,0.001731474,0.0004272555,0.000004908391,0.0007836898,0.0007137556],"genre_scores_gemma":[0.9473127,0.00004759964,0.05050973,0.0008571757,0.0005049994,0.00007964689,0.00001105146,0.00006192813,0.0006151908],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8977216,"threshold_uncertainty_score":0.999798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04785952324490614,"score_gpt":0.2623840421220411,"score_spread":0.2145245188771349,"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."}}