{"id":"W2098830914","doi":"10.1109/oceans.2008.5151919","title":"A study of channel capacity for a seabed underwater acoustic sensor network","year":2008,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software deployment; Bathymetry; Computer science; Underwater; Seabed; Underwater acoustic communication; Wireless sensor network; Channel (broadcasting); Acoustic sensor; Channel capacity; Telecommunications; Marine engineering; Computer network; Acoustics; Engineering; Geology; Oceanography","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":[0.0007265775,0.0005636265,0.0004008959,0.0009692163,0.0006946325,0.0009217783,0.0006069246,0.0007114849,0.003603786],"category_scores_gemma":[0.00605065,0.0002475213,0.0002899877,0.0009425845,0.001229075,0.002161559,0.000609383,0.0007552991,0.0003533958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001730667,"about_ca_system_score_gemma":0.0008670164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007650738,"about_ca_topic_score_gemma":0.00517649,"domain_scores_codex":[0.9993242,0.0001841403,0.00001229705,0.00007800279,0.0001936643,0.0002076669],"domain_scores_gemma":[0.9937037,0.00496771,0.0003350599,0.000153074,0.0007205624,0.0001200063],"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":[0.00007827202,0.00002582792,0.0009488703,0.0001225985,0.00002488857,0.0002051332,0.0001484281,0.9293625,0.009821866,0.04713412,0.001100017,0.01102734],"study_design_scores_gemma":[0.000002233476,0.00004108643,0.0006442101,0.00002777471,0.00001331894,0.0001487384,0.00009025529,0.9866732,0.002022875,0.009278196,0.001038396,0.00001979702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3864535,0.005323804,0.5442968,0.002089532,0.0001601442,0.0001291495,0.0007156038,0.0003801938,0.06045121],"genre_scores_gemma":[0.9824729,0.001838564,0.009836161,0.0001089244,0.00007658308,0.00006659266,0.00009914634,0.00007440137,0.005426718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007650738,"threshold_uncertainty_score":0.01521242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07057665491113935,"score_gpt":0.23494318874368,"score_spread":0.1643665338325407,"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."}}