{"id":"W2964083059","doi":"10.1007/s00200-019-00381-3","title":"On extremal double circulant self-dual codes of lengths 90–96","year":2019,"lang":"en","type":"article","venue":"Applicable Algebra in Engineering Communication and Computing","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Japan Society for the Promotion of Science","keywords":"Circulant matrix; Dual (grammatical number); Mathematics; Code (set theory); Combinatorics; Discrete mathematics; Computer science","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":[0.0005687051,0.0001417173,0.0002230947,0.0001775834,0.00007327203,0.00006531084,0.0006774165,0.00005935348,0.000005173586],"category_scores_gemma":[0.000008616938,0.0001521429,0.00004704557,0.0004680264,0.00002300831,0.0001411303,0.0004110966,0.0002376539,0.00000966815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002169913,"about_ca_system_score_gemma":0.00001409929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002652007,"about_ca_topic_score_gemma":0.000001208457,"domain_scores_codex":[0.9990423,0.00004800294,0.0002840821,0.0002643303,0.0001419982,0.0002193348],"domain_scores_gemma":[0.9985,0.0004561603,0.0000942714,0.0008708438,0.00003030762,0.00004845606],"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":[0.00001162298,0.000112866,0.001931974,0.00008640578,0.0000234652,8.711788e-7,0.0008374664,0.02774063,0.002990961,0.9590921,0.0000104252,0.007161143],"study_design_scores_gemma":[0.001351181,0.000120983,0.005778454,0.0003030215,0.000008226103,0.00002025584,0.0001089779,0.9764299,0.004680768,0.009783869,0.001015779,0.0003985708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8918137,0.0003981376,0.1054599,0.00009033706,0.00005942149,0.0002310565,5.020757e-7,0.0002515791,0.001695378],"genre_scores_gemma":[0.9800426,0.00005094304,0.01980682,0.00005875271,0.000009996868,0.00001121858,0.000003163073,0.00001113738,0.000005411446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9493083,"threshold_uncertainty_score":0.6204209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006535158867155918,"score_gpt":0.20682669579713,"score_spread":0.2002915369299741,"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."}}