{"id":"W1969617574","doi":"10.1121/1.4781060","title":"Data error estimation for matched-field geoacoustic inversion","year":2004,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Bayesian probability; Inversion (geology); Observational error; Computer science; Posterior probability; Nonlinear system; Algorithm; Inverse problem; Errors-in-variables models; Statistics; Mathematics; 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":[0.003988478,0.000786798,0.0006451,0.00134255,0.0004206332,0.0008239772,0.001259277,0.001112525,0.001731006],"category_scores_gemma":[0.02053023,0.0004085382,0.0004663483,0.001052364,0.0008292261,0.001868071,0.001890398,0.0008170931,0.000701768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008235277,"about_ca_system_score_gemma":0.001516789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004084858,"about_ca_topic_score_gemma":0.00620292,"domain_scores_codex":[0.9981064,0.0006992121,0.00009850808,0.0001879759,0.0008355359,0.00007240141],"domain_scores_gemma":[0.9953169,0.002801007,0.0003410478,0.0005435442,0.0009427509,0.00005476913],"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":[0.0003845602,0.0001221413,0.003459916,0.0003527654,0.0001137299,0.00009000555,0.0001961931,0.5513622,0.01980233,0.05287245,0.002371126,0.3688725],"study_design_scores_gemma":[0.00002754185,0.00002009612,0.0006098216,0.00002333989,0.00001359061,0.00004459744,0.00002280675,0.9752412,0.009654141,0.01246209,0.001857506,0.00002318177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004483984,0.00005639454,0.9946386,0.00004968127,0.00001033024,0.00001886671,0.00004161188,0.0001991863,0.0005012561],"genre_scores_gemma":[0.2048744,0.0001842084,0.7928444,0.00008950229,0.00002557199,0.0002025746,0.0004640856,0.0002050845,0.001110144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004084858,"threshold_uncertainty_score":0.02109331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04685912721556577,"score_gpt":0.301010860213822,"score_spread":0.2541517329982562,"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."}}