{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001748756,0.001075657,0.0009445116,0.001135851,0.0002783017,0.000649238,0.001234611,0.0006457763,0.0006279336],"category_scores_gemma":[0.005844372,0.0004055415,0.0006247438,0.0009769738,0.0003566058,0.0007651611,0.0004636788,0.001125333,0.0003511859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007737952,"about_ca_system_score_gemma":0.0006015311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01009169,"about_ca_topic_score_gemma":0.004231346,"domain_scores_codex":[0.9993283,0.0002520008,0.00003783051,0.0001524177,0.0001504807,0.00007897152],"domain_scores_gemma":[0.9978021,0.001434056,0.0002600432,0.00009311541,0.0003659954,0.00004474497],"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":[0.00003351006,0.00002382945,0.0009394688,0.00002139965,0.0000214254,0.00001988722,0.00001229692,0.9741115,0.0004993225,0.0004687023,0.0002518939,0.02359679],"study_design_scores_gemma":[0.000001023029,0.000007483465,0.0001099207,0.000001755299,0.000002050882,0.000003820781,0.000002654808,0.999488,0.0001650263,0.0001609374,0.00005572322,0.000001580653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04574247,0.0007051417,0.9515058,0.0001468834,0.00004535431,0.00004691385,0.0001013535,0.0008581078,0.0008478716],"genre_scores_gemma":[0.8604389,0.0007159315,0.1359129,0.00007880799,0.00006264764,0.0001561433,0.0004503297,0.000143956,0.002040339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01009169,"threshold_uncertainty_score":0.0200659,"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."}}