{"id":"W4293863367","doi":"10.1109/siu55565.2022.9864805","title":"Packet Loss Rate Prediction for Vehicular Networks with Regression Methods","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Regression analysis; Network packet; Regression; Packet loss; Transmission (telecommunications); Reliability (semiconductor); Heuristic; Linear regression; Wireless ad hoc network; Set (abstract data type); Data set; Polynomial regression; Machine learning; Artificial intelligence; Statistics; Wireless; Power (physics); Computer network; Telecommunications","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0009858565,0.00027833,0.0002882395,0.0001362588,0.001976739,0.0001881641,0.0008169495,0.0001012631,0.00006131316],"category_scores_gemma":[0.000008552483,0.0002697758,0.00005926362,0.0007833428,0.0002383689,0.0002258678,0.0003234189,0.00072482,0.000002112386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001236604,"about_ca_system_score_gemma":0.0001445178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007786823,"about_ca_topic_score_gemma":0.00001494235,"domain_scores_codex":[0.9982786,0.000316412,0.0004045122,0.0004125761,0.0002091791,0.0003787139],"domain_scores_gemma":[0.9980488,0.0002726391,0.0001756784,0.00111841,0.0002435871,0.0001408668],"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":[0.00004447064,0.0001207818,0.0002153573,0.0001222135,0.00008639749,8.8911e-7,0.0003839423,0.7024949,0.001895608,0.004578827,0.0009470215,0.2891096],"study_design_scores_gemma":[0.0004029136,0.00008030453,0.0001555397,0.00006850627,0.0001044185,0.00003337699,0.0005706419,0.9129309,0.0001550365,0.001643058,0.083565,0.0002903253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003219801,0.007878197,0.9856328,0.0006524354,0.0000347861,0.001184322,0.00009713633,0.0004924887,0.0008079797],"genre_scores_gemma":[0.932547,0.001079094,0.05753346,0.000104134,0.00007751015,0.007538941,0.0008670452,0.0000733639,0.000179481],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9293272,"threshold_uncertainty_score":0.9999754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.021647868585994,"score_gpt":0.2796842381702067,"score_spread":0.2580363695842127,"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."}}