{"id":"W2090894796","doi":"10.1088/0266-5611/16/6/302","title":"Geoacoustic model inversion using artificial neural networks","year":2000,"lang":"en","type":"article","venue":"Inverse Problems","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Hydrophone; Artificial neural network; Inversion (geology); A priori and a posteriori; Feedforward neural network; Acoustics; Backpropagation; Estimation theory; Computer science; Geology; Algorithm; Artificial intelligence; Seismology; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002709596,0.0001735773,0.0001555617,0.0001205053,0.0002963362,0.0001474404,0.0002777036,0.0001242971,0.00591289],"category_scores_gemma":[0.00001117802,0.0001551623,0.00006203894,0.0003147895,0.0001580152,0.0003400181,0.00002271792,0.0003264284,0.0005600229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000207837,"about_ca_system_score_gemma":0.00007326988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002543869,"about_ca_topic_score_gemma":0.001340114,"domain_scores_codex":[0.9982541,0.0000759752,0.0002521501,0.0003484351,0.0004250578,0.0006442798],"domain_scores_gemma":[0.9993918,0.00005222566,0.0000397501,0.0002249937,0.00004821031,0.0002429973],"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":[0.00003983452,0.00001657884,0.002244495,0.00001624803,0.000009155218,0.00002137217,0.0001147643,0.9767232,0.0003387411,0.000004460345,0.0003331207,0.02013801],"study_design_scores_gemma":[0.0001709731,0.00005485017,0.0001508729,0.00001339805,0.00002056624,0.00001430395,0.0000295334,0.9964721,0.00003647822,0.002705774,0.0001330001,0.0001981814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8565269,0.00009359782,0.1341886,0.0001866947,0.0003214908,0.0004775453,0.00005283804,0.0001609572,0.007991357],"genre_scores_gemma":[0.994735,0.00003948655,0.004090054,0.0003148012,0.000162696,0.00000101739,0.00006852091,0.000009745909,0.0005786958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1382081,"threshold_uncertainty_score":0.9949958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06205710805920898,"score_gpt":0.2519412470791692,"score_spread":0.1898841390199602,"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."}}