{"id":"W3217286936","doi":"10.1109/ucomms50339.2021.9598150","title":"Channel Quality Prediction for Adaptive Underwater Acoustic Communication","year":2021,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Channel (broadcasting); Computer science; Bathymetry; Underwater; Hidden Markov model; Markov process; Simulation; Telecommunications; Geology; Speech recognition; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002013671,0.00008630046,0.0001200203,0.0000250726,0.0001042776,0.00004674971,0.0001462331,0.00006809517,0.00003815255],"category_scores_gemma":[0.000003157694,0.00008276795,0.00005494858,0.00008270582,0.00001684667,0.0001255819,0.00004758306,0.00007895985,0.00002444269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006649387,"about_ca_system_score_gemma":0.00001346688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003109789,"about_ca_topic_score_gemma":0.0001809522,"domain_scores_codex":[0.9993459,0.00007545132,0.0002555253,0.0001059506,0.00008436431,0.0001328254],"domain_scores_gemma":[0.9992201,0.00009633445,0.00002525869,0.0004780499,0.0001410392,0.00003917659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00016497,0.000642953,0.0007511313,0.001272199,0.001337931,0.000003006129,0.01287712,0.4079174,0.5028912,0.02388551,0.02073165,0.02752491],"study_design_scores_gemma":[0.001452609,0.00007142193,0.002082801,0.0001048941,0.00006063249,0.00001822506,0.00700603,0.7837166,0.148719,0.01127283,0.04498905,0.0005059775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009573841,0.0004764581,0.9815359,0.0003537219,0.00008154537,0.0002278233,0.00003601262,0.0004132432,0.007301482],"genre_scores_gemma":[0.9931703,0.000128135,0.004960372,0.00008216486,0.00004022856,0.0001244705,0.000125162,0.00002132943,0.001347876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9835964,"threshold_uncertainty_score":0.3375179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0564999518127849,"score_gpt":0.2670073794861606,"score_spread":0.2105074276733757,"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."}}