{"id":"W2126348412","doi":"10.1109/joe.2006.875099","title":"Data Uncertainty Estimation in Matched-Field Geoacoustic Inversion","year":2006,"lang":"en","type":"article","venue":"IEEE Journal of Oceanic Engineering","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Variance (accounting); Gaussian; Estimation theory; Inversion (geology); Variance-based sensitivity analysis; Propagation of uncertainty; Gibbs sampling; Computer science; Statistics; Algorithm; Mathematics; Mathematical optimization; Bayesian probability; One-way analysis of variance; Geology","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":[0.0006245706,0.0000886415,0.0001493486,0.0002599673,0.00002583012,0.00005311883,0.0004158842,0.00005607283,0.0001257034],"category_scores_gemma":[0.0001291654,0.00007635256,0.00002572754,0.0002189822,0.00001031979,0.0004129735,0.00001762499,0.0003203535,0.00001867871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002763733,"about_ca_system_score_gemma":0.00007034099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000452438,"about_ca_topic_score_gemma":0.0002284665,"domain_scores_codex":[0.9989269,0.0000211722,0.0003416745,0.0001098102,0.0003647274,0.0002357287],"domain_scores_gemma":[0.9993696,0.0002477926,0.00008592912,0.0001763169,0.0000481079,0.00007225174],"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":[0.00001436959,0.00000978028,0.005869898,0.00003565994,0.000007153229,0.00008617088,0.00001937969,0.9900919,0.00142427,0.000001641946,0.0009417571,0.001498069],"study_design_scores_gemma":[0.0002562717,0.00006736958,0.01888357,0.00007638682,0.00001130209,0.00007032559,0.00002411983,0.9797888,0.0004844047,0.0001541211,0.000102871,0.00008048305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6187545,0.0002104348,0.3799984,0.0001637904,0.0005266426,0.00006673119,0.00002655123,0.00001687966,0.0002360837],"genre_scores_gemma":[0.9907464,0.00003552758,0.00897485,0.0000192465,0.0001740718,3.836769e-8,0.000024303,0.000004369039,0.00002122298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3719919,"threshold_uncertainty_score":0.3113567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02264226635600374,"score_gpt":0.2474107193959181,"score_spread":0.2247684530399144,"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."}}