{"id":"W2142027180","doi":"10.1093/bioinformatics/bts716","title":"SCARPA: scaffolding reads with practical algorithms","year":2012,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Toronto","keywords":"Scaffold; Contig; Computer science; Algorithm; Heuristic; Process (computing); Genome; Biology; Artificial intelligence; Programming language; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.003320474,0.001775038,0.001202403,0.00136983,0.001227577,0.001985764,0.003519379,0.001968425,0.02333614],"category_scores_gemma":[0.02029159,0.001374326,0.001300073,0.002440616,0.001222591,0.002621665,0.002742869,0.002869919,0.01310343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008162389,"about_ca_system_score_gemma":0.002009928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002234197,"about_ca_topic_score_gemma":0.002927397,"domain_scores_codex":[0.9977508,0.0006718731,0.0001614162,0.0006731067,0.0005985653,0.0001443134],"domain_scores_gemma":[0.9920524,0.004875791,0.0004045917,0.001602931,0.0008691957,0.0001950368],"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":[0.001045255,0.0002101913,0.001994208,0.001719121,0.0001338651,0.000458365,0.0005362143,0.1378799,0.02474176,0.04476888,0.1028577,0.6836544],"study_design_scores_gemma":[0.0004089683,0.0002240139,0.0005398804,0.0001567544,0.000045749,0.0004929111,0.0001396941,0.8002643,0.03030698,0.09174236,0.07559622,0.00008210001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003258471,0.0002346725,0.9559729,0.0002211257,0.0000702211,0.000146789,0.001317039,0.03639914,0.002379645],"genre_scores_gemma":[0.03128113,0.0001801882,0.9575377,0.0001908541,0.00006545763,0.0004353722,0.004324458,0.003972357,0.002012387],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02333614,"threshold_uncertainty_score":0.07806718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160935312870881,"score_gpt":0.2760012413538531,"score_spread":0.2543918882251443,"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."}}