{"id":"W4283267624","doi":"10.1186/s12859-022-04790-z","title":"RResolver: efficient short-read repeat resolution within ABySS","year":2022,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"National Human Genome Research Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Genome British Columbia; Canada's Michael Smith Genome Sciences Centre; Genome Canada","keywords":"Computational biology; DNA microarray; Resolution (logic); Computer science; Biology; Genetics; Programming language; Gene; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003748616,0.0001626941,0.0001447417,0.00005429837,0.0003820965,0.00002633699,0.0002497158,0.00006368013,0.00002770742],"category_scores_gemma":[0.0000466096,0.0001582582,0.0001113595,0.0001134021,0.00006679018,8.523087e-7,0.0005261127,0.0001136018,0.00001302788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004173572,"about_ca_system_score_gemma":0.00008689796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000912857,"about_ca_topic_score_gemma":0.00001488901,"domain_scores_codex":[0.9988525,0.00004392772,0.0003980991,0.0001955021,0.0002395663,0.0002704239],"domain_scores_gemma":[0.9992937,0.00001263718,0.0001179412,0.0004562664,0.0000527554,0.00006665252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001127126,0.0007901131,0.03055543,0.0004507012,0.0005853889,0.00001321883,0.008199123,0.7770848,0.1084139,0.005699019,0.04968556,0.01739557],"study_design_scores_gemma":[0.002098957,0.001737391,0.01151045,0.00002492304,0.0001466821,0.0001991851,0.007732505,0.4855131,0.01726742,0.0002009037,0.472127,0.001441482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9513678,0.002101434,0.02009027,0.00007295362,0.001146273,0.0006321692,0.000223152,0.00002817608,0.0243378],"genre_scores_gemma":[0.9692816,0.00009969773,0.02857528,0.0004231149,0.0001779192,0.00009078041,0.0002342821,0.00002895273,0.001088385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4224415,"threshold_uncertainty_score":0.6453582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01872862315502454,"score_gpt":0.2343019722389533,"score_spread":0.2155733490839287,"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."}}