{"id":"W4391340262","doi":"10.1101/gr.278157.123","title":"GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis","year":2024,"lang":"en","type":"article","venue":"Genome Research","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Oak Ridge National Laboratory; National Institute on Deafness and Other Communication Disorders; National Institutes of Health; National Cancer Institute; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Jackson Laboratory","keywords":"Biology; Genetics; Computational biology; Genome; Interoperation; Quantitative trait locus; Inbred strain; Evolutionary biology; Gene; Interoperability; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000661883,0.0001010741,0.0001037216,0.0002805661,0.000175485,0.0002237372,0.0002564343,0.0001118815,0.00006898726],"category_scores_gemma":[0.00005713897,0.00009130972,0.00003351584,0.0004652089,0.00003674405,0.00001218687,0.0003480502,0.0001155669,0.00003877218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000155888,"about_ca_system_score_gemma":0.00005270874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004541264,"about_ca_topic_score_gemma":0.0003120868,"domain_scores_codex":[0.9987223,0.0001200333,0.0001537316,0.0005279562,0.0002365276,0.0002394742],"domain_scores_gemma":[0.9992713,0.00002986631,0.0000204933,0.0004382416,0.0001584211,0.00008172361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004186314,0.00004137051,0.002277909,0.0001295625,0.0004144859,0.000007251179,0.0007246446,0.00163104,0.9784234,0.00005666355,0.0006452822,0.0156065],"study_design_scores_gemma":[0.0009315632,0.0004290288,0.4315816,0.00006314017,0.0006614259,0.00003420496,0.001558726,0.3833207,0.01015732,0.0004290031,0.1699434,0.0008898329],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765693,0.002888949,0.01917965,0.0003261965,0.00005259168,0.0002428072,0.0003597728,0.00002280213,0.0003579501],"genre_scores_gemma":[0.991465,0.0008710435,0.003571851,0.00005855587,0.0002121245,0.000008916165,0.002502514,0.00001143455,0.00129852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9682661,"threshold_uncertainty_score":0.3723502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09878888857044088,"score_gpt":0.3451100272419541,"score_spread":0.2463211386715132,"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."}}