{"id":"W2114776857","doi":"10.1109/glocom.2009.5425228","title":"Adaptive Probabilistic Medium Access in MPR-Capable Ad-Hoc Wireless Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer network; Computer science; Wireless ad hoc network; Multiple Access with Collision Avoidance for Wireless; Ad hoc wireless distribution service; Network packet; Aloha; Random access; Probabilistic logic; Vehicular ad hoc network; Distributed computing; Optimized Link State Routing Protocol; Stochastic geometry models of wireless networks; Mobile ad hoc network; Wireless network; Wireless; Transmission (telecommunications); Throughput; Telecommunications; Routing protocol","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"],"consensus_categories":[],"category_scores_codex":[0.0005180544,0.0002910822,0.0003755654,0.0001349945,0.00009472796,0.0003627417,0.002480941,0.0001874016,0.00009333481],"category_scores_gemma":[0.00002969365,0.0002584673,0.00007228011,0.001317887,0.00007112256,0.001283434,0.000483458,0.0004534911,0.00005011315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001649755,"about_ca_system_score_gemma":0.0001497495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005059201,"about_ca_topic_score_gemma":0.0005095794,"domain_scores_codex":[0.9973323,0.0001351201,0.0004700325,0.0008286886,0.0004090619,0.0008247959],"domain_scores_gemma":[0.9983096,0.0002304077,0.0001290159,0.0009966289,0.0001149434,0.0002193955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001063273,0.0006721148,0.000995464,0.00002336966,0.0000309527,0.0002399849,0.0007209183,0.2949986,0.0000494087,0.2101375,0.01981351,0.4722119],"study_design_scores_gemma":[0.0005064343,0.0002416721,0.007320374,0.00007120702,0.000004755881,0.00001172226,0.00001797867,0.9763596,0.00007107478,0.01408499,0.0009389109,0.0003712635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008392695,0.0007277241,0.9680479,0.001697053,0.000749915,0.0009437329,6.172303e-7,0.0005261585,0.01891422],"genre_scores_gemma":[0.9859964,0.0001246618,0.01127222,0.001600964,0.0001857131,0.00008769444,0.00000299115,0.00001680233,0.0007125792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9776037,"threshold_uncertainty_score":0.9999868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01918527115189082,"score_gpt":0.2547745549380247,"score_spread":0.2355892837861339,"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."}}