{"id":"W4400638857","doi":"10.1093/bioadv/vbae098","title":"loco-pipe: an automated pipeline for population genomics with low-coverage whole-genome sequencing","year":2024,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Institute of General Medical Sciences; North Pacific Research Board","keywords":"Pipeline (software); Genomics; Computer science; Streamlines, streaklines, and pathlines; Population; Set (abstract data type); Pipeline transport; Genome; Computational biology; Biology; Engineering; Genetics; Medicine; Operating system; Gene; Programming language","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":[0.005268511,0.002173491,0.001862919,0.00245823,0.001621078,0.002674913,0.003580974,0.001412437,0.03570952],"category_scores_gemma":[0.01167732,0.002326324,0.002921171,0.001840069,0.001094177,0.002612645,0.004549201,0.00422691,0.03131896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008843173,"about_ca_system_score_gemma":0.00328438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003004903,"about_ca_topic_score_gemma":0.005161978,"domain_scores_codex":[0.9979891,0.0003724275,0.000145222,0.0008043027,0.0004915826,0.0001973167],"domain_scores_gemma":[0.9963845,0.001586823,0.0003623164,0.0006960665,0.0006254662,0.0003448415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001738307,0.000196057,0.009172753,0.004549984,0.001083813,0.000614134,0.001760623,0.0112988,0.1324856,0.01606878,0.5796013,0.2414297],"study_design_scores_gemma":[0.001049579,0.0003812127,0.01520337,0.0005969519,0.0004926911,0.001157593,0.0003113522,0.1205663,0.125183,0.05601612,0.6783074,0.0007343814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00613911,0.0004807055,0.6989354,0.0004699196,0.0003530217,0.0005375248,0.05450689,0.2341869,0.00439048],"genre_scores_gemma":[0.03208385,0.0004741276,0.7521909,0.001000548,0.0001729545,0.002274775,0.1374216,0.06726786,0.007113366],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03570952,"threshold_uncertainty_score":0.1194603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0092613412800591,"score_gpt":0.2608134375166964,"score_spread":0.2515520962366373,"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."}}