{"id":"W3202525117","doi":"10.1101/2021.10.07.463580","title":"Pseudofinder: detection of pseudogenes in prokaryotic genomes","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; Gordon and Betty Moore Foundation; National Aeronautics and Space Administration; National Science Foundation","keywords":"Pseudogene; Genome; Biology; Gene; ENCODE; Genetics; Computational biology; Identification (biology); Selection (genetic algorithm); Negative selection; Phylogenetic tree; Bacterial genome size; Evolutionary biology; Computer science; Ecology; Artificial intelligence","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":[0.003439908,0.002059732,0.001068721,0.003881278,0.0008919571,0.002167566,0.001739734,0.001597556,0.004995131],"category_scores_gemma":[0.0103015,0.0009590006,0.00111934,0.001798362,0.0007196139,0.002031489,0.002095237,0.00135045,0.004258465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004621553,"about_ca_system_score_gemma":0.001026057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004968458,"about_ca_topic_score_gemma":0.0005425189,"domain_scores_codex":[0.9979494,0.0003562105,0.000188288,0.0006381918,0.0007351559,0.0001326298],"domain_scores_gemma":[0.9971002,0.001386869,0.0004690528,0.000447625,0.0004109922,0.000185221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004617077,0.0003670906,0.03828922,0.00459354,0.0007405528,0.001707745,0.001457261,0.01159439,0.5090593,0.0117307,0.1232632,0.29258],"study_design_scores_gemma":[0.0003980876,0.000512298,0.02533038,0.0004309287,0.0002221767,0.003037774,0.0003600852,0.2221829,0.5804248,0.02388703,0.142632,0.0005817184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.1214509,0.001676785,0.5758371,0.0005805785,0.0006197849,0.0002961006,0.0321473,0.2639913,0.00340011],"genre_scores_gemma":[0.1935773,0.0005869826,0.7071317,0.000367275,0.0001184889,0.0005401834,0.07249916,0.02113666,0.004042171],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.004995131,"threshold_uncertainty_score":0.01819217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01097006265036804,"score_gpt":0.2066996351157698,"score_spread":0.1957295724654018,"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."}}