{"id":"W2017929570","doi":"10.1109/icc.2013.6654987","title":"Pedestrian collision avoidance in vehicular networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"National Research Council Canada","keywords":"Dedicated short-range communications; Computer science; Pedestrian; Collision avoidance; Computer network; Network packet; Channel (broadcasting); Collision; Vehicular ad hoc network; Intersection (aeronautics); Transmission (telecommunications); Control channel; Real-time computing; Wireless; Wireless ad hoc network; Telecommunications; Computer security; Engineering; Transport engineering","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":[0.0001625658,0.0001852511,0.0002046518,0.00007603551,0.00003419305,0.00007427707,0.0001881199,0.0001661645,0.0003535403],"category_scores_gemma":[0.00001529874,0.0001766365,0.00005441168,0.0003676995,0.00002034607,0.0002421741,0.00003781332,0.0003277796,0.0004753056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009931029,"about_ca_system_score_gemma":0.000009822582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001837092,"about_ca_topic_score_gemma":0.0003129608,"domain_scores_codex":[0.9988478,0.00003528018,0.0002674568,0.0002023757,0.0001525912,0.0004944414],"domain_scores_gemma":[0.9994548,0.00005491144,0.00001781445,0.0003143379,0.00002770461,0.0001304631],"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":[0.000001291591,0.00001058515,0.001684975,0.00001163502,0.00000830223,0.00001712185,0.00002014626,0.9858832,0.0002272588,0.0001207605,0.009499489,0.002515253],"study_design_scores_gemma":[0.0003369023,0.00001317458,0.01048529,0.00004105092,0.000003416896,0.000007474358,0.00002069112,0.9818563,0.0002261906,0.0001301549,0.006657427,0.0002219118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9513003,0.001623136,0.03302768,0.0001480643,0.0004411442,0.0005683542,6.505448e-7,0.000537981,0.01235264],"genre_scores_gemma":[0.9976655,0.0002272495,0.001265681,0.000116169,0.0001721572,0.00007053779,0.00001054806,0.00004709282,0.0004250996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04636511,"threshold_uncertainty_score":0.7203026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0035930027097487,"score_gpt":0.1676643983741896,"score_spread":0.164071395664441,"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."}}