{"id":"W1939794221","doi":"10.1109/icc.1999.765545","title":"Unique word detection in the presence of frequency offset","year":2003,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Constant false alarm rate; Frequency offset; False alarm; Speech recognition; Offset (computer science); Detection theory; Algorithm; Subspace topology; Statistic; Matched filter; Artificial intelligence; Detector; Mathematics; Orthogonal frequency-division multiplexing; Telecommunications; Statistics","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":[0.001619051,0.0006157315,0.001550727,0.00182591,0.0006146423,0.001054116,0.001184874,0.001758434,0.0008779257],"category_scores_gemma":[0.01393955,0.000378414,0.000425272,0.0009474764,0.0009897518,0.002821923,0.002175778,0.00113405,0.0006833386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000334104,"about_ca_system_score_gemma":0.0005612521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003590601,"about_ca_topic_score_gemma":0.00047339,"domain_scores_codex":[0.9977832,0.0004942692,0.0001529876,0.0004143995,0.0009654686,0.0001896731],"domain_scores_gemma":[0.9901677,0.005696363,0.001361551,0.0009001021,0.001613977,0.0002602996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001737218,0.0001336703,0.01648051,0.0005476907,0.0001804453,0.002740083,0.0007049927,0.03975833,0.1543392,0.0238461,0.0016409,0.7578909],"study_design_scores_gemma":[0.00006388085,0.0009507922,0.00717788,0.00009388856,0.000182387,0.007350524,0.0003765692,0.7130498,0.2307823,0.03319919,0.006549605,0.0002231754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1632017,0.001071233,0.8328242,0.0001916253,0.0002036765,0.00003333227,0.00009327169,0.0006704258,0.001710423],"genre_scores_gemma":[0.7194578,0.0005462257,0.2774729,0.0001892576,0.0001757435,0.00004445103,0.0002439939,0.00009372496,0.001775985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00182591,"threshold_uncertainty_score":0.008562505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01451151061267257,"score_gpt":0.2407013764498169,"score_spread":0.2261898658371443,"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."}}