{"id":"W2054767096","doi":"10.1007/s10044-006-0032-z","title":"Breadth-first search strategies for trie-based syntactic pattern recognition","year":2006,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Trie; Computer science; Computation; Prefix; String (physics); Heuristic; Representation (politics); Algorithm; Element (criminal law); Dynamic programming; Data structure; Theoretical computer science; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001608286,0.0001205184,0.0001777053,0.0002056648,0.0003442424,0.0004349707,0.0003051549,0.00004459034,0.00002702647],"category_scores_gemma":[0.000001849119,0.0001067724,0.0001161565,0.0006189088,0.00003112468,0.0002964814,0.00006608079,0.0000641689,0.00002164333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001857851,"about_ca_system_score_gemma":0.00003236026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001643335,"about_ca_topic_score_gemma":0.000711787,"domain_scores_codex":[0.9989454,0.00002838211,0.0002275453,0.0004368024,0.0001695039,0.0001923747],"domain_scores_gemma":[0.9991153,0.0001565067,0.00009233112,0.0004597691,0.0001117374,0.00006437319],"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":[0.000003770628,0.0002821013,0.01375005,0.0000687064,0.00013924,0.000001369386,0.00005755384,0.001014238,0.0002807435,0.001744814,0.000454016,0.9822034],"study_design_scores_gemma":[0.0006569474,0.00005809819,0.04629894,0.0000263305,0.0004399238,0.000003012147,0.0001031909,0.9372141,0.001388822,0.007179364,0.006237799,0.0003934414],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00624664,0.00006668633,0.9921411,0.000843682,0.00001353678,0.0003067078,0.000176847,0.00007223095,0.000132574],"genre_scores_gemma":[0.9899319,0.00002347822,0.008655149,0.0001803066,0.0001169041,0.00044803,0.0006136992,0.00000768337,0.00002281468],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9836853,"threshold_uncertainty_score":0.4354052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02461615076739881,"score_gpt":0.2738431311713298,"score_spread":0.249226980403931,"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."}}