{"id":"W2170924562","doi":"10.1121/1.4934517","title":"Efficient localization and spectral estimation of an unknown number of ocean acoustic sources using a graphics processing unit","year":2015,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Graphics processing unit; Curse of dimensionality; Simulated annealing; Inversion (geology); Bayesian probability; Algorithm; Rendering (computer graphics); Artificial intelligence; Geology; Parallel computing","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001195179,0.0001091034,0.00027402,0.00004087539,0.0001510702,0.00002547572,0.0003635823,0.00005978569,0.0000236817],"category_scores_gemma":[0.0002472497,0.00005985953,0.00009799575,0.0005265554,0.001238733,0.0001056256,0.00004718557,0.0002654485,3.740676e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000139869,"about_ca_system_score_gemma":0.0002463683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005109424,"about_ca_topic_score_gemma":0.000008107805,"domain_scores_codex":[0.9981307,0.0002305945,0.0004779447,0.00008310271,0.0008697692,0.0002078834],"domain_scores_gemma":[0.9983591,0.0003225185,0.0005927545,0.0001495107,0.0004377314,0.0001383984],"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":[0.0001060231,0.00006753653,0.00529592,0.0001120692,0.00003889544,4.431701e-7,0.002131393,0.9870819,0.0008937115,6.944323e-7,0.00004266288,0.004228787],"study_design_scores_gemma":[0.0002699521,0.0002706365,0.002525595,0.0001036192,0.0002163298,0.00007907904,0.003582863,0.9913654,0.0005784114,0.0009381842,0.000004219562,0.0000656519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5367178,0.00009352084,0.462973,0.00009154022,0.00002865915,0.00006486175,0.000007996078,0.000002655547,0.00002003472],"genre_scores_gemma":[0.9610219,0.00004346656,0.03881551,0.0000603527,0.00004640162,2.684434e-8,0.0000016805,0.000005629527,0.000005023142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4243042,"threshold_uncertainty_score":0.4564165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03099813207302045,"score_gpt":0.2905618658175585,"score_spread":0.2595637337445381,"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."}}