{"id":"W2008903345","doi":"10.1007/s10586-007-0020-0","title":"An exact parallel algorithm to compare very long biological sequences in clusters of workstations","year":2007,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Workstation; Algorithm; Heuristic; Sequence (biology); Smith–Waterman algorithm; Computation; Dynamic programming; Cluster (spacecraft); Quadratic equation; Time complexity; Parallel computing; Parallel algorithm; Sequence alignment; Mathematics; Artificial intelligence; Biology","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":[0.001528961,0.0009590709,0.001114223,0.001990734,0.001477431,0.001272293,0.002906069,0.001074698,0.00521914],"category_scores_gemma":[0.00533601,0.0007175482,0.0006676551,0.003563941,0.0008888617,0.002144081,0.001908076,0.0009343419,0.001442146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008904269,"about_ca_system_score_gemma":0.001736947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003811317,"about_ca_topic_score_gemma":0.004487255,"domain_scores_codex":[0.9987885,0.0002210919,0.0001170038,0.0002406939,0.0005280761,0.0001046086],"domain_scores_gemma":[0.9981122,0.0007339665,0.00008479328,0.0004906403,0.0005040352,0.00007435285],"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":[0.0007472371,0.0002454762,0.001617197,0.0002707208,0.0001865615,0.0002180391,0.0002472388,0.1761879,0.0246196,0.02387787,0.01015485,0.7616271],"study_design_scores_gemma":[0.0001938108,0.0001900034,0.000876308,0.00001476339,0.00007063763,0.0003012554,0.0000774181,0.93572,0.01730573,0.03864811,0.006565563,0.00003642305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01810812,0.000317404,0.9763282,0.0001042118,0.0001268313,0.0001143739,0.0001625238,0.003125515,0.001612871],"genre_scores_gemma":[0.07943055,0.0001531268,0.9165012,0.0000799522,0.00006213148,0.000304604,0.0005048788,0.0002495234,0.002714007],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00521914,"threshold_uncertainty_score":0.01745975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03297818096897284,"score_gpt":0.3145297221361031,"score_spread":0.2815515411671303,"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."}}