{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003335004,0.0003563138,0.0003895998,0.0006992599,0.0001774077,0.0006563034,0.0005509523,0.0003730168,0.0003258189],"category_scores_gemma":[0.0110458,0.0004732086,0.0003899088,0.000553026,0.0004672619,0.001069683,0.000774731,0.0003512738,0.00007366064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004563592,"about_ca_system_score_gemma":0.0005181471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006839754,"about_ca_topic_score_gemma":0.008256637,"domain_scores_codex":[0.9992387,0.0003004727,0.00003411056,0.0001290447,0.0002347256,0.00006297366],"domain_scores_gemma":[0.9956857,0.003253758,0.0004292594,0.0002674149,0.0003161169,0.00004787905],"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":[0.000130409,0.00006887493,0.0459572,0.00004358672,0.0001414566,0.00009148205,0.0001331163,0.8959355,0.008244217,0.002868905,0.0001118099,0.04627354],"study_design_scores_gemma":[0.000004632311,0.00001720197,0.008560707,0.0000032981,0.00000842165,0.00001637644,0.0000193025,0.9888738,0.001015684,0.001418169,0.00005195972,0.00001042769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6328185,0.00007736737,0.3661744,0.00006745201,0.000002963154,0.00001550189,0.00009914272,0.0001282132,0.000616543],"genre_scores_gemma":[0.9731718,0.00004533521,0.02631939,0.00001047552,0.000006099028,0.00001668225,0.0002174497,0.00003138057,0.0001813238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006839754,"threshold_uncertainty_score":0.01763737,"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."}}