{"id":"W4399324179","doi":"10.3390/jmse12060943","title":"Predictive Modeling of Future Full-Ocean Depth SSPs Utilizing Hierarchical Long Short-Term Memory Neural Networks","year":2024,"lang":"en","type":"article","venue":"Journal of Marine Science and Engineering","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Term (time); Artificial neural network; Long short term memory; Computer science; Environmental science; Artificial intelligence; Recurrent neural network","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":[0.0003237286,0.000662424,0.0003537625,0.0003705143,0.0002250684,0.0005091953,0.0008658638,0.0005611847,0.0009653649],"category_scores_gemma":[0.001148307,0.0003670947,0.0004667144,0.000488992,0.0003155533,0.0007850272,0.0005930199,0.0009586366,0.0001997977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005886587,"about_ca_system_score_gemma":0.0009073474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02140764,"about_ca_topic_score_gemma":0.02079615,"domain_scores_codex":[0.9998962,0.00001369296,0.000007008036,0.00003375983,0.00002509564,0.00002422133],"domain_scores_gemma":[0.9997274,0.0001114632,0.00004276121,0.0000145144,0.00008885332,0.00001487375],"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":[0.00002569626,0.00001827816,0.001599682,0.00001799994,0.0000178949,0.00003531925,0.00001634285,0.9787738,0.001153742,0.0006455884,0.0003375555,0.01735819],"study_design_scores_gemma":[6.316876e-7,0.000001759378,0.0001285639,7.984502e-7,0.000001185623,0.000001001727,0.000001232369,0.9995852,0.0000935455,0.0001648957,0.00002024264,9.488415e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2749428,0.0007593015,0.7178914,0.000502969,0.0001503239,0.00003632777,0.0006543956,0.001176483,0.003886004],"genre_scores_gemma":[0.9791144,0.0002207432,0.01832125,0.00006252923,0.00002846351,0.00003926055,0.0003739516,0.00003421833,0.001805146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02140764,"threshold_uncertainty_score":0.04256606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01372737666851894,"score_gpt":0.2250340491280474,"score_spread":0.2113066724595284,"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."}}