{"id":"W3017056882","doi":"10.1121/10.0001096","title":"Ship source level estimation and uncertainty quantification in shallow water via Bayesian marginalization","year":2020,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Seabed; Waves and shallow water; Bayesian probability; Sampling (signal processing); Inversion (geology); Point source; Geology; Inverse problem; Noise (video); Acoustics; Computer science; Environmental science; Statistics; Mathematics; Oceanography; Seismology; Telecommunications; Physics; Artificial intelligence","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":[0.0007713949,0.00008741189,0.0001731233,0.00002064191,0.0001315848,0.00003458325,0.0003604927,0.00004965,0.000142739],"category_scores_gemma":[0.0001623588,0.00004195719,0.00007982642,0.0002345433,0.000370334,0.000121565,0.00004051261,0.0003096276,0.000007255818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001478072,"about_ca_system_score_gemma":0.00004570103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004854361,"about_ca_topic_score_gemma":0.00002316671,"domain_scores_codex":[0.9986574,0.0002362163,0.0003370901,0.00009116164,0.0004798906,0.0001982448],"domain_scores_gemma":[0.9991784,0.0003424581,0.0001679011,0.0001145405,0.0001012987,0.00009537397],"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":[0.0001293845,0.00001833666,0.00699332,0.00005209052,0.00002586239,5.005649e-7,0.002421759,0.9473403,0.006161226,3.538616e-7,0.0004648349,0.03639205],"study_design_scores_gemma":[0.0001854665,0.0001334582,0.01615322,0.00002125304,0.00004451314,0.00001430922,0.0007277235,0.9812454,0.0005485669,0.0007652969,0.0001044326,0.00005634069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03157484,0.0000580081,0.9551895,0.01299122,0.00003324898,0.0001135335,0.000007698826,0.000004289356,0.00002765836],"genre_scores_gemma":[0.9779165,0.0001076555,0.02111842,0.0007719244,0.00005516018,1.627219e-7,0.000006145623,0.00000404026,0.00001993084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9463417,"threshold_uncertainty_score":0.1710965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03810155369680931,"score_gpt":0.2595034503820681,"score_spread":0.2214018966852588,"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."}}