{"id":"W4229715808","doi":"10.1002/wcm.525","title":"An efficient MAC protocol for cooperative diversity in mobile ad hoc networks","year":2007,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Computer network; Cooperative diversity; Relay; Throughput; Fading; Blocking (statistics); Wireless ad hoc network; Mobile ad hoc network; Protocol (science); Diversity gain; Channel (broadcasting); Wireless; Telecommunications; Network packet","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":[0.002150912,0.0007319723,0.0007455418,0.0006922332,0.0006168587,0.001239749,0.001607405,0.0008146348,0.0008246868],"category_scores_gemma":[0.004068097,0.0003082466,0.0003402559,0.0006571299,0.0008154662,0.001137437,0.001146887,0.001128733,0.0002975094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005140682,"about_ca_system_score_gemma":0.0007211874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002979238,"about_ca_topic_score_gemma":0.0003876389,"domain_scores_codex":[0.9983984,0.0005992323,0.0001020693,0.0001255553,0.0006676252,0.0001071411],"domain_scores_gemma":[0.9978066,0.0007695237,0.0003595707,0.0004362269,0.000566683,0.00006141307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003377181,0.0003944841,0.001216054,0.000822461,0.0005060938,0.0009301369,0.000578985,0.2396913,0.1170878,0.3375314,0.01416759,0.286736],"study_design_scores_gemma":[0.00009886764,0.0003258279,0.0003615983,0.00005776135,0.0001262631,0.0004798643,0.00004037178,0.9161186,0.0194438,0.03340719,0.02948553,0.00005417366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01076461,0.001503292,0.9840735,0.0002720762,0.0002619822,0.0002308294,0.00003952975,0.0003919093,0.002462339],"genre_scores_gemma":[0.6144938,0.001838742,0.3758049,0.000469167,0.0003957442,0.001245912,0.0001840657,0.00005184039,0.005515819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002150912,"threshold_uncertainty_score":0.01137525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05114150699580787,"score_gpt":0.3517409192006437,"score_spread":0.3005994122048358,"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."}}