{"id":"W2107744299","doi":"10.1109/18.825842","title":"On a conjectured ideal autocorrelation sequence and a related triple-error correcting cyclic code","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cyclic code; Golomb coding; BCH code; Autocorrelation; Sequence (biology); Mathematics; Code (set theory); Polynomial code; Ideal (ethics); Pseudorandom binary sequence; Binary number; Constant-weight code; Linear code; TRACE (psycholinguistics); Discrete mathematics; Error detection and correction; Algorithm; Decoding methods; Computer science; Block code; Arithmetic; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007238436,0.0001939973,0.0001544948,0.0003943472,0.0005336882,0.0001790991,0.0002726676,0.0001360941,0.0004520477],"category_scores_gemma":[0.00002649508,0.000185477,0.0001060784,0.0006482349,0.00009459745,0.001693947,0.00000182552,0.0004059066,0.0002906532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005869641,"about_ca_system_score_gemma":0.0000442946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007406452,"about_ca_topic_score_gemma":0.000002975364,"domain_scores_codex":[0.9986379,0.0002385902,0.0004346867,0.0002208597,0.0002382846,0.0002296872],"domain_scores_gemma":[0.9988399,0.0004989149,0.000149509,0.0003614195,0.00005282144,0.00009740963],"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":[0.000413164,0.00007596792,0.000003259294,0.00001674122,0.00004649914,0.00000238504,0.008902442,0.0489899,0.00009973046,0.1739658,0.00007552303,0.7674087],"study_design_scores_gemma":[0.00465121,0.001338739,0.0007586158,0.000354504,0.00009614349,0.00054301,0.0008681851,0.603582,0.007678052,0.3757589,0.00317882,0.001191839],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07701477,0.00001014203,0.9113634,0.0001618753,0.0007582308,0.0004180419,0.00001898453,0.0005760142,0.009678531],"genre_scores_gemma":[0.9979317,0.00001160022,0.0008439949,0.0007156124,0.000008842057,0.00006708717,0.000005887739,0.000008284357,0.0004070054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9209169,"threshold_uncertainty_score":0.7563533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01221284767400123,"score_gpt":0.240450298437279,"score_spread":0.2282374507632777,"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."}}