{"id":"W2043891929","doi":"10.5555/777092.777206","title":"Memory-efficient A* heuristics for multiple sequence alignment","year":2002,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Computer science; Octree; Pairwise comparison; Heuristic; Sequence (biology); Offset (computer science); Theoretical computer science; Algorithm; 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":[0.001165933,0.001146927,0.001078886,0.001796315,0.001684303,0.001965364,0.002293532,0.001505624,0.006605504],"category_scores_gemma":[0.006558711,0.0008096892,0.00133524,0.003032807,0.0008078477,0.002772817,0.002178215,0.001697733,0.002995126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001063977,"about_ca_system_score_gemma":0.001919269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003469929,"about_ca_topic_score_gemma":0.005653118,"domain_scores_codex":[0.9986425,0.0005574682,0.0001702748,0.0001999851,0.0002971081,0.0001326789],"domain_scores_gemma":[0.9961206,0.002254744,0.0003655149,0.0006880602,0.0004220598,0.0001490145],"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":[0.0005932164,0.0003875947,0.001048097,0.0005029551,0.0001356321,0.0003020687,0.0004601848,0.2213287,0.0119113,0.1236712,0.01847984,0.6211792],"study_design_scores_gemma":[0.0001610692,0.000213926,0.0003240523,0.00007032076,0.0000615809,0.0002818615,0.0001827959,0.8347296,0.007624913,0.1345557,0.02172976,0.00006449366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009146342,0.0003710829,0.9834625,0.000106081,0.00006786772,0.0001200124,0.0001335693,0.002564828,0.004027767],"genre_scores_gemma":[0.05239797,0.0002394138,0.9444134,0.00008263961,0.00002690489,0.0002591721,0.0003202225,0.0003198512,0.001940445],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006605504,"threshold_uncertainty_score":0.02209765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1007810243537694,"score_gpt":0.299922876042191,"score_spread":0.1991418516884216,"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."}}