{"id":"W2068333739","doi":"10.1121/1.1419087","title":"Quantifying uncertainty in geoacoustic inversion. II. Application to broadband, shallow-water data","year":2002,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Inversion (geology); Independent and identically distributed random variables; Gibbs sampling; Broadband; Bayesian probability; Gaussian; Sampling (signal processing); Posterior probability; Sonar; Inverse problem; Computer science; Statistics; Geology; Mathematics; Random variable; Seismology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002774182,0.0004588724,0.0004042419,0.001163733,0.000263644,0.0007557516,0.0006698837,0.0006992285,0.0004897481],"category_scores_gemma":[0.01173311,0.0003362739,0.0003970329,0.0008860476,0.001406145,0.001065584,0.001542202,0.0006383668,0.0001238983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004796677,"about_ca_system_score_gemma":0.0007126915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002723434,"about_ca_topic_score_gemma":0.001945683,"domain_scores_codex":[0.9985312,0.000536599,0.00007476939,0.0001658806,0.0006289661,0.00006249743],"domain_scores_gemma":[0.9957609,0.003345066,0.0002636779,0.0003670282,0.0002308955,0.00003256799],"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":[0.00008812113,0.00003743719,0.005905493,0.0002323038,0.00008067,0.0001565754,0.000220592,0.7954555,0.01688379,0.02357323,0.0004738952,0.1568925],"study_design_scores_gemma":[0.000007161762,0.00003723375,0.003070506,0.00003261767,0.00001321859,0.0001258087,0.00004264557,0.9605436,0.00802996,0.02650539,0.001563373,0.00002851137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02547851,0.0002247558,0.9731386,0.0001128109,0.00002183921,0.00003285384,0.00009020484,0.0001378793,0.0007624964],"genre_scores_gemma":[0.682506,0.0005676962,0.3154344,0.00009823885,0.00007880521,0.0001395292,0.000337021,0.0001134546,0.0007248613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002774182,"threshold_uncertainty_score":0.01467144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0579844932756222,"score_gpt":0.2828296186202131,"score_spread":0.2248451253445909,"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."}}