{"id":"W2131310218","doi":"10.1109/icassp.1978.1170419","title":"A programmable sonar signal processor","year":2005,"lang":"en","type":"article","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Research Council Canada","keywords":"Replica; Sonar; Computer science; Bandwidth (computing); Digital signal processor; Signal processing; SIGNAL (programming language); Computer hardware; Doppler effect; Digital signal processing; Range (aeronautics); Electronic engineering; Telecommunications; Engineering; Artificial intelligence; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002623926,0.0004789451,0.0004841215,0.0004901672,0.0003258015,0.0006477488,0.001361106,0.000367489,0.01841923],"category_scores_gemma":[0.0005609556,0.0002377613,0.0002144573,0.0003699577,0.0001967836,0.0005062915,0.0003570552,0.0005703141,0.005048247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000268285,"about_ca_system_score_gemma":0.0006953199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005629121,"about_ca_topic_score_gemma":0.0003726411,"domain_scores_codex":[0.9996381,0.00004218033,0.00002566538,0.00008654891,0.0001586297,0.00004897415],"domain_scores_gemma":[0.9996723,0.00006348754,0.00002384571,0.00005623708,0.0001432002,0.00004089676],"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":[0.001152342,0.0003646594,0.002299226,0.0009547086,0.000085528,0.0005267979,0.0001533552,0.02684675,0.3251315,0.01866375,0.02908484,0.5947365],"study_design_scores_gemma":[0.0007379362,0.003264429,0.004006952,0.0001288332,0.000243733,0.002957529,0.00006973362,0.2323178,0.4041227,0.00457672,0.3474022,0.0001713784],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.062287,0.0007267023,0.8802569,0.0002170994,0.0004260561,0.0009652541,0.001079079,0.02703983,0.02700212],"genre_scores_gemma":[0.4557633,0.0006411238,0.5019847,0.0004188284,0.0001532916,0.00117383,0.001782806,0.0005236959,0.03755861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01841923,"threshold_uncertainty_score":0.06161851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02424050588326132,"score_gpt":0.250707967925307,"score_spread":0.2264674620420457,"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."}}