{"id":"W2048531488","doi":"10.1121/1.3056553","title":"Model selection and Bayesian inference for high-resolution seabed reflection inversion","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":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Seabed; Inversion (geology); Bayesian inference; Inference; Bayesian probability; Geology; Computer science; Reflection (computer programming); Selection (genetic algorithm); Artificial intelligence; Model selection; Algorithm; Oceanography; Seismology","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":[0.01258714,0.001237107,0.001711772,0.001852486,0.001327771,0.001622734,0.002709995,0.001597909,0.002113135],"category_scores_gemma":[0.04663499,0.001663493,0.001290038,0.001782157,0.001712776,0.002308633,0.002062146,0.002824917,0.0007544031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168813,"about_ca_system_score_gemma":0.002699386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007802256,"about_ca_topic_score_gemma":0.009726514,"domain_scores_codex":[0.9942697,0.004225178,0.0001997593,0.0003973173,0.0007458942,0.0001621149],"domain_scores_gemma":[0.9775935,0.01926618,0.0007689591,0.0009645004,0.001188327,0.0002185488],"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":[0.0001123402,0.00008178485,0.001598138,0.0001033201,0.0001566054,0.0001230504,0.0001145089,0.8969539,0.001190837,0.03298341,0.0008151216,0.06576698],"study_design_scores_gemma":[0.00002206306,0.000009986065,0.0001581029,0.000007093029,0.000008411042,0.00001564038,0.000006335935,0.9795044,0.0002584396,0.01980047,0.0001979674,0.00001114692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004091791,0.00006471005,0.9952313,0.00008830521,0.000008234762,0.00002180128,0.00003096466,0.000211857,0.0002509696],"genre_scores_gemma":[0.2280142,0.0002899319,0.7691361,0.0001711785,0.0001168062,0.000451939,0.0005462481,0.0002608484,0.001012717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01258714,"threshold_uncertainty_score":0.06656796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02752332214658817,"score_gpt":0.2805054303384893,"score_spread":0.2529821081919011,"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."}}