{"id":"W1562413084","doi":"10.1109/ccece.1995.526281","title":"Underwater signal prediction and parameter estimation using artificial neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Attenuation; Artificial neural network; SIGNAL (programming language); Computer science; Underwater; Backpropagation; Estimation theory; Underwater acoustic communication; Amplitude; Reflection (computer programming); Identification (biology); Algorithm; Artificial intelligence; Geology; Optics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005067799,0.0005648612,0.0003434979,0.0002867329,0.0001906491,0.0004046377,0.0003897171,0.0005356771,0.0004807704],"category_scores_gemma":[0.001979674,0.0002262147,0.0002372301,0.0002783115,0.0002385797,0.0006070484,0.0003262065,0.0004761111,0.00012231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003674918,"about_ca_system_score_gemma":0.0002936392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004228193,"about_ca_topic_score_gemma":0.002872662,"domain_scores_codex":[0.999832,0.00005779363,0.00001091801,0.00003248937,0.00004831487,0.00001847083],"domain_scores_gemma":[0.9993692,0.0003620569,0.00007584563,0.00004716338,0.0001340595,0.00001165471],"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":[0.0000926588,0.00003929067,0.001086457,0.00002502923,0.0000184403,0.0000319721,0.0000204657,0.9409035,0.006406904,0.0003962938,0.0001421902,0.05083684],"study_design_scores_gemma":[0.000001433074,0.0000102615,0.0001055175,8.675636e-7,0.000001661016,0.000001962029,0.00000131271,0.9986903,0.001068919,0.0000897362,0.00002632276,0.000001771738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2604302,0.0001706031,0.7368306,0.000125079,0.000028992,0.00003830903,0.00005739164,0.0008394633,0.001479335],"genre_scores_gemma":[0.939711,0.00008263897,0.05902338,0.00001913363,0.00001113301,0.00005991822,0.00008416021,0.00001716489,0.0009915126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004228193,"threshold_uncertainty_score":0.008407176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06519109945409564,"score_gpt":0.2537198649277112,"score_spread":0.1885287654736156,"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."}}