{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001277707,0.0004601192,0.0004275618,0.0007161744,0.0004261236,0.0008069309,0.0006790264,0.0007659223,0.001653171],"category_scores_gemma":[0.004468563,0.0002635814,0.0001554764,0.001122413,0.0005455345,0.00168857,0.0004131176,0.0005142123,0.0007380092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000508148,"about_ca_system_score_gemma":0.0003042253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006880832,"about_ca_topic_score_gemma":0.0009079874,"domain_scores_codex":[0.9988513,0.0003726798,0.00005245515,0.00009336742,0.0005451569,0.00008496061],"domain_scores_gemma":[0.9984099,0.000698749,0.0001670453,0.0003373649,0.0003571714,0.00002967374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001260252,0.0002221231,0.009188477,0.0005375726,0.00006088711,0.0004443586,0.0002120895,0.09588119,0.3464628,0.04003959,0.004615551,0.501075],"study_design_scores_gemma":[0.00005989934,0.0008315482,0.005957686,0.00009283772,0.00009114068,0.0008601786,0.0001037783,0.639748,0.3284243,0.01226207,0.0114821,0.00008655383],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1882351,0.00345217,0.7992172,0.0006233534,0.0002358214,0.0001335201,0.0001384925,0.001933774,0.006030544],"genre_scores_gemma":[0.8416009,0.001051307,0.1543195,0.0001042077,0.00007327535,0.00005600562,0.0001014943,0.00008604141,0.0026074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001653171,"threshold_uncertainty_score":0.0067572,"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."}}