{"id":"W2131989567","doi":"10.1142/s0219720004000661","title":"PATTERNHUNTER II: HIGHLY SENSITIVE AND FAST HOMOLOGY SEARCH","year":2004,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Topological and Geometric Data Analysis","field":"Computer Science","cited_by":231,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Bioinformatics Solutions (Canada); University of Waterloo","funders":"","keywords":"Homology (biology); Sensitivity (control systems); Smith–Waterman algorithm; Computer science; Mathematics; Computational biology; Biology; Genetics; Sequence alignment; Engineering; Gene","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":[0.0002782641,0.00008451053,0.0002218243,0.0003113805,0.0001203682,0.00005809373,0.0001981006,0.00006405447,0.000003777855],"category_scores_gemma":[0.00003131901,0.00005609251,0.00004844918,0.0002491141,0.0001591194,0.0002783682,0.0002884979,0.0001396548,0.00000569099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001566535,"about_ca_system_score_gemma":0.00005110879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001623403,"about_ca_topic_score_gemma":0.000001389304,"domain_scores_codex":[0.9992189,0.00003493366,0.0003880688,0.00009178992,0.00012091,0.0001453708],"domain_scores_gemma":[0.9992833,0.000156495,0.0001962849,0.0000670593,0.0001995524,0.00009730808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006415814,0.0002915802,0.005135865,0.00004761014,0.0005157046,0.0001480656,0.004676554,0.0145182,0.0002236602,0.5432392,0.0003595729,0.4307798],"study_design_scores_gemma":[0.006760806,0.009252112,0.2047695,0.0001023922,0.0001340886,0.01087304,0.001609517,0.309951,0.0006665055,0.4505881,0.004314837,0.0009779815],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3620874,0.0001053494,0.6339501,0.003635868,0.00007696646,0.00002287627,0.00001386684,0.000005475261,0.000102118],"genre_scores_gemma":[0.9137625,0.00009322923,0.08505571,0.001030957,0.00003796385,2.533086e-7,0.00000936139,0.00000118988,0.00000887199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.551675,"threshold_uncertainty_score":0.2287386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01108296267543167,"score_gpt":0.2464544238746993,"score_spread":0.2353714611992676,"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."}}