{"id":"W2121305702","doi":"10.1093/bioinformatics/btp054","title":"Fast computation of neighbor seeds","year":2009,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Western University","funders":"","keywords":"Computation; Heuristic; Computer science; Exponential function; Current (fluid); Class (philosophy); Algorithm; Exponential growth; Sensitivity (control systems); k-nearest neighbors algorithm; Polynomial; Time complexity; Artificial intelligence; Mathematics","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":[0.001028512,0.0008528384,0.001214146,0.00154026,0.0009027714,0.001283486,0.001362133,0.001066571,0.007374756],"category_scores_gemma":[0.01104822,0.0005294587,0.0006488857,0.001754547,0.0008347807,0.002601106,0.001367262,0.0008703306,0.003030251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007744299,"about_ca_system_score_gemma":0.001252077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001754832,"about_ca_topic_score_gemma":0.002498325,"domain_scores_codex":[0.9988295,0.0002471495,0.0000968431,0.0002665796,0.0004577579,0.0001021808],"domain_scores_gemma":[0.9954359,0.002453477,0.0002979461,0.0007548378,0.0008723832,0.0001853974],"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":[0.0009860361,0.0001904762,0.004484749,0.000808422,0.0000997399,0.0004748187,0.0004406777,0.2177209,0.03895636,0.07279228,0.01895582,0.6440898],"study_design_scores_gemma":[0.0001312797,0.0001898178,0.0009118731,0.0000486551,0.00003872943,0.0005647282,0.00009151675,0.864194,0.02664495,0.09638144,0.01075616,0.00004686735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03081224,0.0004879363,0.962226,0.0001553637,0.00008980211,0.0001171677,0.0002673994,0.002459495,0.00338465],"genre_scores_gemma":[0.1678687,0.0002393227,0.8283814,0.00007030662,0.00005844832,0.0001180564,0.00102784,0.0004506258,0.001785313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007374756,"threshold_uncertainty_score":0.02467102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009562008365173272,"score_gpt":0.233085311769209,"score_spread":0.2235233034040357,"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."}}