{"id":"W2496793544","doi":"10.1111/1755-0998.12577","title":"Linking genomics and population genetics with R","year":2016,"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":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Medical Research Council; National Institute for Health and Care Research; National Science Foundation","keywords":"Genomics; Biology; Linkage disequilibrium; Population genomics; Data science; Software; Population; Linkage (software); Population genetics; Genetics; Haplotype; Genome; Computer science; Allele","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":[0.00007950024,0.0001068663,0.0001014354,0.00003596807,0.00009971037,0.00001984361,0.00009431575,0.0001221555,0.00001126957],"category_scores_gemma":[0.00001582113,0.00007353842,0.00002539459,0.00002603567,0.00008643537,0.000001435875,0.00009782278,0.00003041592,0.000005073719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000585528,"about_ca_system_score_gemma":0.00001393788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006859413,"about_ca_topic_score_gemma":0.00003653336,"domain_scores_codex":[0.9993542,0.00003994823,0.00009366073,0.0002667668,0.00006132545,0.0001840565],"domain_scores_gemma":[0.9996938,0.00001136837,0.00005375001,0.0001501599,0.00002904657,0.00006193267],"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":[0.00007081589,0.0000185205,0.4649793,0.00001014628,0.0000565513,0.00001865079,0.00005903071,0.00006392889,0.5281784,0.00008446467,0.0001103653,0.006349856],"study_design_scores_gemma":[0.002239227,0.001609935,0.7141569,0.00006299825,0.0001113593,0.0002532329,0.0001808029,0.00006305594,0.1175258,0.0006170914,0.1624709,0.0007087631],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972891,0.0005058748,0.001267706,0.000267722,0.00003209404,0.00007744979,0.00000983802,0.00001131847,0.0005388891],"genre_scores_gemma":[0.9977328,0.0001937999,0.001267486,0.000335625,0.00006053309,0.000003511704,0.00002073625,0.00001123982,0.0003743182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4106526,"threshold_uncertainty_score":0.299881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005262554079151847,"score_gpt":0.1896689098655802,"score_spread":0.1844063557864284,"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."}}