{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00395592,0.0007046309,0.0007714197,0.001267526,0.0004764089,0.001014482,0.001350775,0.001133062,0.0006005233],"category_scores_gemma":[0.01532983,0.0004685875,0.0006534947,0.0008991551,0.001316839,0.002627326,0.001769079,0.000717502,0.0001758287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008883917,"about_ca_system_score_gemma":0.000943315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002953858,"about_ca_topic_score_gemma":0.00344963,"domain_scores_codex":[0.9972038,0.001057306,0.0001104857,0.0003040445,0.001206004,0.0001184184],"domain_scores_gemma":[0.9964482,0.002587706,0.000232524,0.0003329091,0.0003610942,0.00003756503],"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":[0.0001426973,0.00004022303,0.001901674,0.0001164357,0.00007940248,0.00009778621,0.0001586344,0.816072,0.006710805,0.04081338,0.0004814369,0.1333855],"study_design_scores_gemma":[0.00001942942,0.00002918653,0.0006134584,0.00002406445,0.00001899108,0.00004843407,0.00003796648,0.9608129,0.009343867,0.02753594,0.00148043,0.00003530479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009840125,0.0001078312,0.9893287,0.00007600949,0.00001203442,0.00001139929,0.00002499497,0.000101371,0.0004975175],"genre_scores_gemma":[0.5825067,0.0003030151,0.4158129,0.0001538907,0.000057859,0.00009292667,0.0001841797,0.0001321617,0.0007563283],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00395592,"threshold_uncertainty_score":0.02092111,"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."}}