{"id":"W2165065548","doi":"10.1109/icct.2003.1209817","title":"Transmitter identification using embedded spread spectrum sequences","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Transmitter; Spread spectrum; Direct-sequence spread spectrum; Computer science; Interference (communication); Noise (video); Electronic engineering; SIGNAL (programming language); Truncation (statistics); Frequency domain; Signal-to-noise ratio (imaging); Telecommunications; Algorithm; Code division multiple access; Engineering; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003356612,0.000371729,0.0003892712,0.0003898108,0.0002805609,0.0004453441,0.000465884,0.000462936,0.001150263],"category_scores_gemma":[0.0008864027,0.0001742525,0.000186317,0.0002336866,0.000221179,0.0006807337,0.0003999016,0.0004235471,0.0007289387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001659203,"about_ca_system_score_gemma":0.0003369448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002853596,"about_ca_topic_score_gemma":0.0004305399,"domain_scores_codex":[0.9996217,0.00008147352,0.00002882234,0.0000608904,0.0001814808,0.00002552003],"domain_scores_gemma":[0.9995373,0.0001085211,0.00008758803,0.00008093649,0.0001613524,0.00002432979],"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":[0.0007043101,0.0001940754,0.004254362,0.0003900918,0.00009534118,0.0007106204,0.0003981087,0.07035241,0.4062321,0.02216691,0.001584929,0.4929167],"study_design_scores_gemma":[0.0000931402,0.0011619,0.002114171,0.00008430757,0.000105789,0.002123416,0.00006480201,0.7223379,0.2543798,0.003593997,0.01388027,0.00006050076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06530657,0.0002017261,0.9308636,0.00006386696,0.0000966658,0.00008217474,0.00003131686,0.001114774,0.002239267],"genre_scores_gemma":[0.6314929,0.0002690557,0.3615594,0.000100632,0.00007945092,0.0000860267,0.0001180632,0.00003769226,0.006256971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001150263,"threshold_uncertainty_score":0.003847957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01861582193872813,"score_gpt":0.2658185656043078,"score_spread":0.2472027436655796,"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."}}