{"id":"W2042909471","doi":"10.1109/camsap.2013.6714087","title":"Frequency domain distributed OFDM source detection","year":2013,"lang":"en","type":"article","venue":"","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Detector; Orthogonal frequency-division multiplexing; Computer science; Frequency domain; Noise (video); Algorithm; Multiplexing; Time domain; Noise power; Signal-to-noise ratio (imaging); Computational complexity theory; Power (physics); Electronic engineering; Telecommunications; Artificial intelligence; Engineering; Physics; Channel (broadcasting)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001215472,0.0001551811,0.000134281,0.00007394295,0.0002088578,0.0004072771,0.0004676592,0.0001000356,0.0003621524],"category_scores_gemma":[0.000023041,0.000133026,0.00008519692,0.0006922045,0.00003715976,0.0006634343,0.0001142087,0.0001643574,0.001033987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006571694,"about_ca_system_score_gemma":0.00001653811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004167685,"about_ca_topic_score_gemma":0.00003349628,"domain_scores_codex":[0.9987026,0.00006010243,0.0002495256,0.0003732829,0.0002454335,0.0003690511],"domain_scores_gemma":[0.9990391,0.00004663027,0.00007675581,0.0005079101,0.0001470474,0.000182628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005753241,0.000288021,0.0005240417,0.00001583424,0.00009532105,0.00002467268,0.0002750984,0.001381696,0.03278152,0.07057823,0.02634688,0.8676829],"study_design_scores_gemma":[0.001647011,0.0003946626,0.02046634,0.00002345319,0.00001483724,0.0002713061,0.0004160614,0.6957183,0.01782212,0.1885197,0.07323592,0.001470227],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02102542,0.00002344697,0.9715558,0.0006565354,0.0005163623,0.0001957801,0.000004228716,0.0007961495,0.005226266],"genre_scores_gemma":[0.9470449,0.000004746081,0.05170489,0.000371911,0.0001365709,0.00004526827,0.0000133227,0.00001065187,0.00066774],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9260195,"threshold_uncertainty_score":0.9997438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005481812934194223,"score_gpt":0.1880747288448169,"score_spread":0.1825929159106227,"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."}}