{"id":"W1993316134","doi":"10.1121/1.3147489","title":"Analyzing lateral seabed variability with Bayesian inference of seabed reflection data","year":2009,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"U.S. Naval Research Laboratory; Office of Naval Research; Defence Research and Development Canada","keywords":"Seabed; Geology; Reflection (computer programming); Bayesian probability; Inference; Bayesian inference; Oceanography; Computer science; Acoustics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00189217,0.0001209361,0.0003227057,0.00002723678,0.0001736098,0.00003084656,0.001277929,0.00005855344,0.0001942301],"category_scores_gemma":[0.0003466259,0.00005627839,0.0001236794,0.0005518991,0.0006620543,0.0002279508,0.00008781315,0.0005569407,0.00000159972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001959983,"about_ca_system_score_gemma":0.0002241324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005166053,"about_ca_topic_score_gemma":0.00001589149,"domain_scores_codex":[0.9979622,0.0004214082,0.0004509441,0.0001378404,0.0007418862,0.000285696],"domain_scores_gemma":[0.9976582,0.0009156859,0.0004281872,0.0006048618,0.0002793761,0.0001137253],"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":[0.001860946,0.0004178376,0.124195,0.0001766221,0.0007267349,0.000004982326,0.00246144,0.7191984,0.05398497,0.000002198993,0.004258801,0.09271212],"study_design_scores_gemma":[0.0002774823,0.0007322864,0.08578379,0.00005589145,0.0002030637,0.00003145088,0.0002787886,0.9101928,0.0006243768,0.00168829,0.00004666798,0.00008510787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07761205,0.00005250849,0.9187298,0.003047989,0.00006858406,0.0001408256,0.00005347658,0.000007847024,0.0002868722],"genre_scores_gemma":[0.9465547,0.0001328426,0.05296578,0.0002329187,0.00007794446,4.191814e-8,0.000005099652,0.00000296582,0.0000277584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8689426,"threshold_uncertainty_score":0.2439367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03227919162802747,"score_gpt":0.2990509603563242,"score_spread":0.2667717687282967,"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."}}