{"id":"W4414624944","doi":"10.1093/bib/bbaf512","title":"A novel pairwise sequence alignment algorithm for similarity search in massive datasets","year":2025,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Pairwise comparison; Multiple sequence alignment; Preprocessor; Sequence (biology); Sequence alignment; Similarity (geometry); Alignment-free sequence analysis; Range (aeronautics)","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":[0.001481988,0.001472377,0.001903463,0.003697231,0.001179348,0.001295629,0.002474849,0.001196521,0.00347402],"category_scores_gemma":[0.005566662,0.0005159687,0.001276244,0.007611335,0.0005518871,0.003430491,0.00189018,0.001737807,0.002920373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005646634,"about_ca_system_score_gemma":0.002140648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002537449,"about_ca_topic_score_gemma":0.003225246,"domain_scores_codex":[0.9974104,0.0005238387,0.0003001695,0.0004927333,0.001147076,0.0001258268],"domain_scores_gemma":[0.9988587,0.0003992328,0.0001190321,0.0002290679,0.0003356372,0.00005825501],"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":[0.000333319,0.0002082345,0.001496619,0.0004804529,0.0002467266,0.0003021559,0.0001619324,0.04111282,0.02227053,0.01301652,0.01906439,0.9013063],"study_design_scores_gemma":[0.0002101924,0.0004372142,0.001591883,0.00006759688,0.00008951,0.00152937,0.0001464777,0.9046914,0.02033725,0.03075504,0.04005415,0.00008991605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005999508,0.001133222,0.9881449,0.0001444055,0.000151872,0.0002131063,0.0005297717,0.002840244,0.0008430354],"genre_scores_gemma":[0.02840981,0.0005933812,0.967031,0.00009889933,0.0001066498,0.0004061157,0.002104668,0.0001619211,0.001087553],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003697231,"threshold_uncertainty_score":0.01162177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03438104331734072,"score_gpt":0.305489804256393,"score_spread":0.2711087609390523,"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."}}