{"id":"W2016532782","doi":"10.1109/wowmom.2010.5534953","title":"AGC-based RF Fingerprints in Wireless Sensor Networks for authentication","year":2010,"lang":"en","type":"article","venue":"","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Fingerprint (computing); Computer science; Wireless sensor network; Transmitter; Authentication (law); Radio frequency; Fingerprint recognition; Reliability (semiconductor); Wireless; Noise (video); Node (physics); Simple (philosophy); Real-time computing; Embedded system; Computer network; Engineering; Artificial intelligence; Telecommunications; Computer security","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":[0.0003789817,0.00009931226,0.0001046527,0.000137629,0.00006919388,0.0001098758,0.0004496249,0.0001083265,0.00002334719],"category_scores_gemma":[0.00007449463,0.00009804242,0.00004386186,0.0003233717,0.00003037093,0.0002527615,0.00003799905,0.0001423376,0.00002993978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003608036,"about_ca_system_score_gemma":0.00005983177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001967784,"about_ca_topic_score_gemma":0.0001054463,"domain_scores_codex":[0.9989716,0.00003011683,0.0002629297,0.0003639598,0.0001585426,0.0002127913],"domain_scores_gemma":[0.998886,0.0002803143,0.0001082931,0.0005304571,0.0001327449,0.0000621678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003264014,0.0003648981,0.02029666,0.00003819613,0.00001010212,0.000001867762,0.0003159451,0.03096501,0.2707595,0.5574946,0.0003915242,0.1193291],"study_design_scores_gemma":[0.0003134665,0.00001174512,0.0686351,0.000008296621,0.000001548719,5.683474e-7,0.000003548765,0.9190207,0.01039454,0.00127061,0.0002242191,0.0001157025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2131698,0.000001061959,0.7839325,0.00188237,0.0003412174,0.000306055,6.317443e-7,0.0001284912,0.0002379109],"genre_scores_gemma":[0.9234922,3.218552e-7,0.07592652,0.0002071135,0.00007059532,0.0001011779,0.00001072739,0.000009600552,0.0001817359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8880556,"threshold_uncertainty_score":0.3998054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02008475397394538,"score_gpt":0.2608147735057739,"score_spread":0.2407300195318285,"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."}}