{"id":"W2518374209","doi":"10.1093/bioinformatics/btw463","title":"CoLoRMap: Correcting Long Reads by Mapping short reads","year":2016,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Leverage (statistics); Sequence assembly; Source code; Reference genome; DNA sequencing; Algorithm; Data mining; Computational biology; Biology; Artificial intelligence; Genetics; Gene; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003125992,0.002575718,0.001098568,0.001990654,0.000982188,0.001659523,0.003029643,0.001488015,0.007033289],"category_scores_gemma":[0.0103671,0.0009189209,0.001343741,0.002514502,0.001146701,0.001927256,0.002235251,0.002468047,0.005882981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006728897,"about_ca_system_score_gemma":0.001461239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002615302,"about_ca_topic_score_gemma":0.003844108,"domain_scores_codex":[0.9969811,0.0005169796,0.00009903767,0.0009825819,0.001306781,0.0001136053],"domain_scores_gemma":[0.9950169,0.001957003,0.0007706287,0.0008884156,0.001167247,0.0001997377],"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":[0.0008308241,0.0001822637,0.007315343,0.002698436,0.0005953057,0.0005426055,0.000587651,0.05792108,0.1441288,0.0120384,0.08429907,0.6888602],"study_design_scores_gemma":[0.0001822493,0.0004081727,0.006942087,0.0003058905,0.0002893232,0.001766791,0.0001896689,0.4453686,0.3480358,0.03223323,0.1638484,0.0004297313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01095206,0.001328376,0.9474032,0.0003535014,0.0006192889,0.0001543688,0.002307126,0.03543375,0.001448382],"genre_scores_gemma":[0.04333961,0.0007010422,0.9403022,0.0004625342,0.0001645218,0.0002538811,0.00472334,0.005343836,0.00470899],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007033289,"threshold_uncertainty_score":0.0235287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01404787610589953,"score_gpt":0.2284586485761806,"score_spread":0.2144107724702811,"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."}}