{"id":"W3135363434","doi":"10.1111/1755-0998.13372","title":"SimBit: A high performance, flexible and easy‐to‐use population genetic simulator","year":2021,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Biology; Population; Selection (genetic algorithm); Selfing; Epistasis; Effective population size; Coalescent theory; Evolutionary biology; Statistics; Computer science; Genetic variation; Genetics; Machine learning; Mathematics; Demography","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":[0.0000737648,0.0001546642,0.0001757005,0.00006395424,0.0001729248,0.00006501082,0.0001106051,0.0001734388,0.00004590557],"category_scores_gemma":[0.00009998595,0.0001615372,0.00004848564,0.0001014501,0.00004749993,0.000003633925,0.000230793,0.00006602873,0.00002062961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007018785,"about_ca_system_score_gemma":0.00002764194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005157613,"about_ca_topic_score_gemma":0.00003447567,"domain_scores_codex":[0.998946,0.00007726324,0.0001607567,0.000431495,0.0001060675,0.000278385],"domain_scores_gemma":[0.9994626,0.0000172379,0.00004871148,0.0002543263,0.00007799383,0.0001391252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001173693,0.00009635477,0.6491559,0.00005405938,0.0001266802,0.0001101095,0.0001157533,0.01112739,0.3358304,0.00009135345,0.0008031395,0.002371512],"study_design_scores_gemma":[0.0005531896,0.0003951644,0.9023408,0.00001594958,0.0000465159,0.00007627714,0.00005808008,0.0003528128,0.05116718,0.00005473831,0.04465645,0.0002828166],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986199,0.0003680133,0.0002944821,0.0001740367,0.00009609882,0.0001174362,0.0000208047,0.00002179908,0.0002874793],"genre_scores_gemma":[0.9946967,0.0000995671,0.001647127,0.001408067,0.00006479522,0.000007616732,0.00009677409,0.00001574764,0.001963576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2846632,"threshold_uncertainty_score":0.6587294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007284709927424676,"score_gpt":0.206891361719606,"score_spread":0.1996066517921813,"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."}}