{"id":"W2043069670","doi":"10.1145/2512840.2512869","title":"Performances of trigonometric chirp spread spectrum modulation in AWGN &amp; rayleigh channels","year":2013,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Additive white Gaussian noise; Chirp spread spectrum; Chirp; Modulation (music); Frequency modulation; Spread spectrum; Spectrum (functional analysis); Electronic engineering; Physics; Mathematics; Computer science; Telecommunications; Direct-sequence spread spectrum; Acoustics; Bandwidth (computing); White noise; Optics; Engineering; Channel (broadcasting)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004946913,0.0001013338,0.0001865827,0.0008494896,0.00005906279,0.000125782,0.001492531,0.0000575097,0.000283157],"category_scores_gemma":[0.00004676799,0.00008632787,0.00004339063,0.002551842,0.00005167499,0.001100684,0.0004390157,0.0001798091,0.0002670069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005766477,"about_ca_system_score_gemma":0.00004459819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005618507,"about_ca_topic_score_gemma":0.00008625352,"domain_scores_codex":[0.9986416,0.0001148182,0.0003625386,0.0002410596,0.0003348668,0.0003051455],"domain_scores_gemma":[0.9985361,0.0002048829,0.0001126416,0.0009856861,0.00009005475,0.00007060555],"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":[0.00003158661,0.0009034569,0.116353,0.0001222124,0.00005765054,0.000001326959,0.002748937,0.1470144,0.002273853,0.1560173,0.003149488,0.5713268],"study_design_scores_gemma":[0.0002804032,0.00003871144,0.1663006,0.00001674657,4.289278e-7,0.00000126665,0.00001266413,0.8271147,0.001690096,0.004041714,0.0003907675,0.0001119124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7491327,0.0002423414,0.239177,0.001701314,0.0001375351,0.000438672,3.359234e-7,0.00007683208,0.009093298],"genre_scores_gemma":[0.9845353,0.0002167264,0.01423232,0.00002730385,0.00004081082,0.00005318867,0.000004302496,0.000006202295,0.0008838685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6801003,"threshold_uncertainty_score":0.3520348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03572364634372791,"score_gpt":0.2758845850497936,"score_spread":0.2401609387060657,"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."}}