{"id":"W2892472137","doi":"10.1109/tmc.2018.2871686","title":"Collision Avoidance Energy Efficient Multi-Channel MAC Protocol for UnderWater Acoustic Sensor Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Handshaking; Computer science; Channel (broadcasting); Computer network; Throughput; Control channel; Network packet; Underwater acoustic communication; Collision; Multiple Access with Collision Avoidance for Wireless; Data transmission; Propagation delay; Underwater; Wireless; Telecommunications; Routing protocol; Telecommunications link; Computer security","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":[0.0009772973,0.0004156082,0.0004722805,0.0005467375,0.0006100138,0.0006584844,0.001078182,0.000468625,0.0007616843],"category_scores_gemma":[0.002162584,0.0001615646,0.0002662666,0.0004897934,0.0003736782,0.0008083497,0.0008695377,0.0006786288,0.0001406698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004578356,"about_ca_system_score_gemma":0.0008429909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006956722,"about_ca_topic_score_gemma":0.0007761843,"domain_scores_codex":[0.998991,0.0002805637,0.00007092286,0.00008528701,0.0004937288,0.00007852079],"domain_scores_gemma":[0.998887,0.0003699459,0.000162748,0.0001198084,0.0004267137,0.0000337237],"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":[0.0006199545,0.0002578268,0.002614457,0.001292229,0.0002879823,0.0009760275,0.0006847814,0.2726304,0.2509711,0.07264071,0.009134873,0.3878897],"study_design_scores_gemma":[0.00006658949,0.0008012782,0.001228254,0.00009651474,0.0001114837,0.0006712165,0.0001194376,0.9021924,0.05981778,0.008595616,0.02621002,0.00008941611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03875445,0.004332303,0.9511859,0.0002780354,0.0002913019,0.0003535813,0.00005149596,0.0005036122,0.004249353],"genre_scores_gemma":[0.7765219,0.001852399,0.2157688,0.0003443676,0.00009323559,0.0008213359,0.0001995648,0.00005074588,0.004347673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001078182,"threshold_uncertainty_score":0.005168498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02723114066299182,"score_gpt":0.2751983905397131,"score_spread":0.2479672498767213,"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."}}