{"id":"W1728571802","doi":"10.1109/icassp.1995.480185","title":"Performance of FH SS radio networks with interference modeled as a mixture of Gaussian and alpha-stable noise","year":2002,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interference (communication); Gaussian noise; Noise (video); Gaussian; Poisson distribution; Transmitter; Computer science; Random variable; Telecommunications; Topology (electrical circuits); Mathematics; Algorithm; Statistics; Physics; Artificial intelligence; Combinatorics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002436907,0.000137377,0.0002630562,0.0001071413,0.00007618959,0.00005848438,0.001334639,0.00007150669,0.00009404984],"category_scores_gemma":[0.00001271944,0.0001021977,0.00002733424,0.0006269056,0.0001885934,0.0005093119,0.0004939234,0.0002595376,0.000004741515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001997979,"about_ca_system_score_gemma":0.00003377173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007348466,"about_ca_topic_score_gemma":0.00002502148,"domain_scores_codex":[0.9987574,0.0001017002,0.0002720472,0.0002723224,0.0002999208,0.0002966578],"domain_scores_gemma":[0.9983175,0.0001622342,0.0001307211,0.00109031,0.0001845613,0.000114622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008651233,0.001768106,0.08506463,0.0008418454,0.0004981892,0.00002593513,0.01257654,0.3993177,0.006536276,0.1177223,0.01579869,0.3589847],"study_design_scores_gemma":[0.0003355986,0.0002984079,0.001637895,0.0001237732,0.000003664723,0.00001113601,0.00002076482,0.995684,0.001587561,0.00003837747,0.0001388307,0.0001200519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6528555,0.002170323,0.3263359,0.0009937712,0.00006888314,0.0004204238,0.000001515605,0.00009802529,0.01705569],"genre_scores_gemma":[0.9766449,0.001408948,0.01953624,0.0000390391,0.00001276871,0.00001711598,0.000001172323,0.000009651576,0.00233022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5963663,"threshold_uncertainty_score":0.4167502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01894408170996563,"score_gpt":0.2322863432503428,"score_spread":0.2133422615403771,"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."}}