{"id":"W1583922594","doi":"10.1007/978-3-540-74553-2_25","title":"An Efficient Algorithm for Identifying the Most Contributory Substring","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Substring; Set (abstract data type); Algorithm; Computer science; Running time; Mathematics; Combinatorics","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":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.002597678,0.0005401039,0.0004815757,0.0006105094,0.0009265002,0.001245571,0.005444319,0.0003145537,0.000007522664],"category_scores_gemma":[0.00007140997,0.000395316,0.0001518142,0.0006140182,0.0005855387,0.0006841243,0.00148956,0.0008093158,0.00001448077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003149967,"about_ca_system_score_gemma":0.0004286057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003604422,"about_ca_topic_score_gemma":0.00003129977,"domain_scores_codex":[0.9954518,0.00003935091,0.0006256149,0.001687641,0.001235362,0.0009602251],"domain_scores_gemma":[0.9959248,0.0009585619,0.0003711259,0.002066113,0.0004423365,0.0002370403],"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":[0.000003444778,0.00002924106,0.000003708308,0.00001345611,0.000007437397,0.00003839932,0.0003163771,0.02767743,0.00007867339,0.007846301,0.00002076035,0.9639648],"study_design_scores_gemma":[0.0003547324,0.0001220711,0.00007430294,0.0002474159,0.000009584044,0.00005057591,5.336442e-7,0.9766946,0.001371456,0.01710445,0.003443807,0.0005264314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003415695,0.001042577,0.9941911,0.0001477214,0.003298115,0.0008004003,0.00004119019,0.0001870024,0.0002577815],"genre_scores_gemma":[0.01304054,0.00003184249,0.984062,0.001264879,0.001377961,0.00002572139,0.00003884002,0.00005148185,0.0001067781],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9634383,"threshold_uncertainty_score":0.9999367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03071493416752983,"score_gpt":0.2923358769748553,"score_spread":0.2616209428073255,"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."}}