{"id":"W3109589850","doi":"10.1109/jiot.2020.3039458","title":"Neighbor Discovery for ProSe and V2X Communications","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Neighbor Discovery Protocol; Computer network; Aloha; Polling; Physical layer; Random access; PHY; Cellular network; Channel (broadcasting); Interference (communication); Throughput; The Internet; Telecommunications; Wireless; Internet Protocol","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002688121,0.0004971941,0.0006798828,0.001087568,0.001040716,0.001508215,0.00110349,0.001047579,0.001258292],"category_scores_gemma":[0.008722847,0.0004085853,0.0004822244,0.001008292,0.001123652,0.002007179,0.0016094,0.0008549747,0.0002345425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001276762,"about_ca_system_score_gemma":0.001135245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00128664,"about_ca_topic_score_gemma":0.001457551,"domain_scores_codex":[0.9975806,0.0007916836,0.0000890503,0.0004474407,0.0009063885,0.0001846714],"domain_scores_gemma":[0.9947889,0.003631505,0.0006402825,0.0004478609,0.0004065294,0.00008494544],"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":[0.0002265065,0.0001041215,0.003493838,0.0003983315,0.00008484138,0.000623191,0.0004279829,0.4382147,0.009263976,0.4278774,0.002681484,0.1166037],"study_design_scores_gemma":[0.00001460281,0.0001490513,0.0005787517,0.00003000963,0.00002964168,0.0007092444,0.00008939231,0.9562305,0.002225094,0.03497369,0.004937701,0.00003231489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04770561,0.00321695,0.9371656,0.0005229699,0.0001261975,0.0001234298,0.00006995742,0.0002001371,0.01086923],"genre_scores_gemma":[0.9557424,0.001962756,0.03853296,0.0001645291,0.0001176446,0.000145455,0.00007667149,0.00003017558,0.003227441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002688121,"threshold_uncertainty_score":0.0142163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02227334698083303,"score_gpt":0.2355156146247651,"score_spread":0.2132422676439321,"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."}}