{"id":"W4307925092","doi":"10.32920/21428709","title":"Interference Excision in Spread Spectrum Communications Using Adaptive Positive Time-Frequency Analysis","year":2022,"lang":"en","type":"preprint","venue":"","topic":"GNSS positioning and interference","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Chirp; Interference (communication); Algorithm; SIGNAL (programming language); Computer science; Direct-sequence spread spectrum; Spread spectrum; Mathematics; Physics; Telecommunications; Optics","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":[0.0002584328,0.000482363,0.0002797289,0.0005154801,0.0002617725,0.0003375339,0.0004922659,0.0004941838,0.001009801],"category_scores_gemma":[0.0008823528,0.000151688,0.0003774481,0.0003571773,0.0003999834,0.000625598,0.0004072679,0.0004461459,0.0005496264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002324551,"about_ca_system_score_gemma":0.0002579233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000527843,"about_ca_topic_score_gemma":0.0008879585,"domain_scores_codex":[0.9997494,0.00004636951,0.00001000815,0.00003573061,0.0001412323,0.00001730859],"domain_scores_gemma":[0.9996728,0.0001492471,0.00004807389,0.0000484564,0.00007025035,0.00001130078],"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":[0.0002770943,0.00007729707,0.00107045,0.0001080084,0.00004696144,0.0003419909,0.000202718,0.05085388,0.2302012,0.009172188,0.001034976,0.7066132],"study_design_scores_gemma":[0.00002364018,0.0002300186,0.001438183,0.00002640967,0.00003455651,0.0008863331,0.00005727265,0.8511761,0.1327581,0.004112722,0.009216631,0.00003996083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02440948,0.0003362114,0.9728178,0.00006150605,0.00003998014,0.00001827492,0.000009632952,0.0003891311,0.001917875],"genre_scores_gemma":[0.2704025,0.0004176401,0.7236282,0.0001211163,0.0000755111,0.000046788,0.00007345211,0.00009628881,0.005138603],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001009801,"threshold_uncertainty_score":0.003378093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03715126471633681,"score_gpt":0.2808703794412016,"score_spread":0.2437191147248648,"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."}}