{"id":"W2514785383","doi":"","title":"Blind channel estimation of underwater acoustic waveguide impulse responses using marine mammal vocalizations","year":2016,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Acoustics; Hydrophone; Impulse (physics); Impulse response; Waveform; Underwater; Deconvolution; Blind deconvolution; Channel (broadcasting); Underwater acoustics; Computer science; Geology; Telecommunications; Physics; Mathematics; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003723849,0.0002097779,0.0002274418,0.0006696489,0.000246278,0.00008046487,0.0003234929,0.0001634145,0.00226729],"category_scores_gemma":[0.0004299676,0.0001591852,0.00005338355,0.0004258853,0.0002946774,0.0002227434,0.00003631741,0.0001283218,0.000152856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001342656,"about_ca_system_score_gemma":0.001250904,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06033571,"about_ca_topic_score_gemma":0.0946847,"domain_scores_codex":[0.9979886,0.000107145,0.0004187668,0.0003140914,0.0004157153,0.0007556736],"domain_scores_gemma":[0.9982408,0.0003724812,0.00009667763,0.0003588118,0.0002657942,0.0006654745],"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":[0.0001220056,0.00001910526,0.01114854,0.00009790088,0.00005385045,0.0001321038,0.0001509857,0.9504095,0.02248781,0.00002090347,0.0004929423,0.01486435],"study_design_scores_gemma":[0.0005633157,0.000101424,0.02411008,0.0000666116,0.00007476008,0.00005890082,0.00008046451,0.9719107,0.0008258831,0.001846597,0.00009603133,0.0002652945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2949086,0.00003087521,0.7011262,0.0003337938,0.0003166565,0.0003191394,0.001160405,0.00003711117,0.001767241],"genre_scores_gemma":[0.987649,0.00004437084,0.01010768,0.000136793,0.0001292841,0.000001297724,0.0001816999,0.00001946726,0.001730388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6927404,"threshold_uncertainty_score":0.9986448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04171762932384993,"score_gpt":0.2722008333467926,"score_spread":0.2304832040229427,"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."}}