{"id":"W2136355314","doi":"10.1109/itw.1989.761413","title":"A Comparison of Trellis Coded SSMA to Convolutionally Coded SSMA","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Convolutional code; Speech recognition; Trellis (graph); Spread spectrum; Decoding methods; Algorithm; Telecommunications; Channel (broadcasting)","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":[0.001139525,0.0003541118,0.0004118679,0.0008726186,0.0003742622,0.001051433,0.0008069394,0.0007988852,0.008286836],"category_scores_gemma":[0.006205034,0.0001129565,0.0002466438,0.0009794688,0.0003091688,0.001049631,0.0004161956,0.0004583308,0.001083829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001098032,"about_ca_system_score_gemma":0.001162965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005245195,"about_ca_topic_score_gemma":0.008080018,"domain_scores_codex":[0.998943,0.0002241241,0.00003665923,0.00006502558,0.0005929079,0.0001382065],"domain_scores_gemma":[0.9942145,0.002832504,0.0001998898,0.0005296543,0.002106573,0.0001168879],"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":[0.009077176,0.0004239386,0.003439444,0.0006030525,0.0002892977,0.0002515555,0.0002192377,0.1491184,0.1770243,0.03622713,0.005966478,0.6173601],"study_design_scores_gemma":[0.000227114,0.001582912,0.004410423,0.00009407193,0.0002159392,0.0006417134,0.0001447141,0.8501303,0.1225051,0.007146887,0.01281729,0.00008357664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4947219,0.007655623,0.4381498,0.0008403716,0.0007648143,0.0002252426,0.0008472123,0.004391189,0.05240401],"genre_scores_gemma":[0.8964241,0.001494321,0.0850993,0.0002187598,0.000142805,0.00005061941,0.0005955623,0.0001942234,0.01578048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008286836,"threshold_uncertainty_score":0.02772224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03785694453760965,"score_gpt":0.3539241045830868,"score_spread":0.3160671600454772,"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."}}