{"id":"W2156979582","doi":"10.1109/icassp.1996.550543","title":"Estimation of delay and Doppler by wavelet transform","year":2002,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Wavelet; Wavelet transform; Estimator; Cascade algorithm; Stationary wavelet transform; Mathematics; Discrete wavelet transform; Second-generation wavelet transform; Sonar; Algorithm; Harmonic wavelet transform; Wavelet packet decomposition; Doppler effect; Computer science; Weighting; Artificial intelligence; Statistics; Acoustics; 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":[],"consensus_categories":[],"category_scores_codex":[0.00004224877,0.00003599883,0.00004790366,0.00002066876,0.00002732359,0.00003378298,0.00009534676,0.00001691304,0.00005793017],"category_scores_gemma":[0.000003786388,0.00002825956,0.000009097121,0.00008785743,0.00001476689,0.0003125827,0.0000111784,0.0000213115,0.000007257823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003243613,"about_ca_system_score_gemma":0.000002918114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000669839,"about_ca_topic_score_gemma":0.000001520909,"domain_scores_codex":[0.9996826,0.000002990706,0.00007654161,0.00008750744,0.00007711184,0.0000732192],"domain_scores_gemma":[0.9998603,0.00001193543,0.00001826471,0.00007089024,0.00001143546,0.00002720871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[3.59516e-7,0.00001557686,0.00003167384,0.0000105167,0.000002063854,6.126841e-7,0.0002830942,0.0000119771,0.002814057,0.0006531674,0.005720776,0.9904561],"study_design_scores_gemma":[0.0002483077,0.00004438722,0.000112828,0.00001044743,0.000002155468,0.00002946115,0.000008347915,0.4443439,0.551681,0.001964128,0.001468477,0.00008656981],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04765837,0.0002289139,0.9375817,0.001326339,0.00001811541,0.00003304007,6.156742e-7,0.00004177774,0.01311111],"genre_scores_gemma":[0.860747,0.00002000115,0.1385962,0.0001616712,0.000003459185,9.125131e-7,4.128007e-7,0.000001403052,0.0004689222],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9903696,"threshold_uncertainty_score":0.1152391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025224799610223,"score_gpt":0.2101644146099876,"score_spread":0.1999121666138853,"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."}}