{"id":"W2509063142","doi":"10.1007/s00453-016-0199-7","title":"Full-Fledged Real-Time Indexing for Constant Size Alphabets","year":2016,"lang":"en","type":"article","venue":"Algorithmica","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Labex Bézout","keywords":"Search engine indexing; Constant (computer programming); Alphabet; Theory of computation; Combinatorics; Symbol (formal); String (physics); String searching algorithm; Matching (statistics); Mathematics; Pattern matching; Computer science; Algorithm; Discrete mathematics; Statistics; Information retrieval; Artificial intelligence","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":[0.0003951757,0.0002343215,0.0002962212,0.00007730331,0.0002486075,0.000135942,0.001094978,0.0001167762,0.00009947719],"category_scores_gemma":[0.0001938128,0.0001565916,0.0001155822,0.0002129992,0.00009485445,0.0007135755,0.0005050353,0.00008215434,0.000246707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007193412,"about_ca_system_score_gemma":0.0001430486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000199778,"about_ca_topic_score_gemma":0.000001753303,"domain_scores_codex":[0.9980479,0.00006197461,0.0003539856,0.0006651849,0.0003144751,0.0005564329],"domain_scores_gemma":[0.9975579,0.001020566,0.0001531515,0.0008942712,0.0001671394,0.0002069975],"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":[0.00006953766,0.0001189046,0.00002332307,0.00001584165,0.00005748323,0.00003200263,0.0001519499,0.000001609049,0.1245946,0.0250117,0.02246292,0.8274601],"study_design_scores_gemma":[0.01536446,0.002556287,0.001582562,0.001057462,0.0001112739,0.0003436832,0.00008905158,0.3680813,0.1429908,0.1239926,0.340052,0.003778436],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003473849,0.00007847439,0.9905344,0.001871937,0.0006652223,0.0004253107,0.0001051964,0.0004302865,0.002415349],"genre_scores_gemma":[0.08429391,0.0001117963,0.9101056,0.0005034318,0.0008080706,0.0001619606,0.00002222348,0.0000645329,0.003928499],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8236817,"threshold_uncertainty_score":0.6385619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01099261713692969,"score_gpt":0.2454894456804947,"score_spread":0.234496828543565,"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."}}