{"id":"W2103020432","doi":"10.1109/pimrc.2008.4699817","title":"Soft output detector for convolutionally encoded Parity Bit selected Multicarrier Direct-Sequence Spread Spectrum system","year":2008,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Parity bit; Viterbi decoder; Computer science; Bit error rate; Algorithm; Detector; Convolutional code; Spread spectrum; Viterbi algorithm; Decoding methods; Electronic engineering; Channel (broadcasting); Telecommunications; Engineering","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":[0.000659525,0.0002596727,0.0003685691,0.0001808571,0.0006780729,0.0001542367,0.002566284,0.0001786442,0.0000256006],"category_scores_gemma":[0.000505525,0.0002403649,0.0001310695,0.001096702,0.0002573327,0.000496425,0.0005967826,0.0003696816,0.000134106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004109807,"about_ca_system_score_gemma":0.000685007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002868232,"about_ca_topic_score_gemma":0.0002343997,"domain_scores_codex":[0.9969155,0.0003563614,0.0005004078,0.0007158886,0.0007140205,0.0007978659],"domain_scores_gemma":[0.9958983,0.001210718,0.0001472269,0.001633607,0.0007752785,0.000334877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000845834,0.002280992,0.06741488,0.001121084,0.001346969,0.0003442542,0.00532124,0.02387997,0.07486288,0.7071813,0.065752,0.04964857],"study_design_scores_gemma":[0.0007123859,0.0001015718,0.00774558,0.0000434739,0.0000064945,0.00007906993,0.00001647577,0.9761221,0.01038392,0.0002001258,0.004219119,0.0003696317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02871012,0.0002776823,0.9632482,0.001305792,0.0002746031,0.001108832,0.00002948156,0.001264057,0.003781258],"genre_scores_gemma":[0.9195392,0.0000421492,0.07787034,0.00009560288,0.0001072074,0.0002742267,0.00001760708,0.00002410555,0.002029624],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9522422,"threshold_uncertainty_score":0.9801797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05516241126362603,"score_gpt":0.282499822659475,"score_spread":0.227337411395849,"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."}}