{"id":"W2053288014","doi":"10.1142/s0129054109007005","title":"AN ADAPTIVE HYBRID PATTERN-MATCHING ALGORITHM ON INDETERMINATE STRINGS","year":2009,"lang":"en","type":"article","venue":"International Journal of Foundations of Computer Science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Strong","keywords":"Indeterminate; Algorithm; Matching (statistics); Computer science; String searching algorithm; Pattern matching; Hybrid algorithm (constraint satisfaction); Successor cardinal; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004868024,0.0003660287,0.0005476868,0.00117085,0.0004354533,0.0008007807,0.001652946,0.0006320347,0.00278028],"category_scores_gemma":[0.002008452,0.0002416566,0.0003122322,0.001678933,0.0004586313,0.001463533,0.0009604343,0.0005582034,0.001207913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003208099,"about_ca_system_score_gemma":0.0005556581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001128545,"about_ca_topic_score_gemma":0.001352741,"domain_scores_codex":[0.9994414,0.00006987173,0.00005098798,0.0001522446,0.0002415728,0.00004377],"domain_scores_gemma":[0.9993292,0.0001783307,0.00004986712,0.0001834223,0.0002273147,0.00003190482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003775931,0.00007387095,0.001104225,0.000085336,0.00003393438,0.0001631905,0.0001454076,0.02682238,0.05159342,0.02128406,0.004053189,0.8942634],"study_design_scores_gemma":[0.00009908651,0.0001950336,0.0008431378,0.00001793271,0.00002921457,0.0006604007,0.00007794154,0.8827962,0.06550475,0.0306379,0.01909533,0.00004308053],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02488876,0.0001339722,0.9719203,0.00007733519,0.0000546123,0.0000517016,0.00006793597,0.001350489,0.001454903],"genre_scores_gemma":[0.1305445,0.00008247371,0.8645102,0.00009214727,0.00002606018,0.00009392987,0.0002637599,0.0001707183,0.004216266],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00278028,"threshold_uncertainty_score":0.009301007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150100370285392,"score_gpt":0.3052397887745584,"score_spread":0.2902297517460192,"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."}}