{"id":"W2727305748","doi":"10.1186/s12864-017-3873-5","title":"LinkImputeR: user-guided genotype calling and imputation for non-model organisms","year":2017,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Canada Research Chairs; Genome Canada","keywords":"Imputation (statistics); Genotype; Missing data; Genotyping; Biology; Software; Computational biology; DNA microarray; Data quality; Genomics; Genome; Data mining; Computer science; Genetics; Statistics; Gene; Machine learning; Mathematics","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":[0.0002876234,0.0001145429,0.0001472929,0.00001751196,0.0005308513,0.00004724304,0.0001902939,0.0002051267,9.131372e-7],"category_scores_gemma":[0.0002367776,0.0001187955,0.00005883798,0.000008475439,0.00004348831,0.000003504727,0.0002021594,0.00004171837,0.000003677527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002069047,"about_ca_system_score_gemma":0.0001171322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002664898,"about_ca_topic_score_gemma":0.0001376236,"domain_scores_codex":[0.999239,0.00001653233,0.000207426,0.0002910098,0.00003228247,0.0002137324],"domain_scores_gemma":[0.9992576,0.00002296451,0.0001952471,0.0003594327,0.0001013422,0.00006337306],"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":[0.0001237687,0.00006520274,0.0984751,0.00009353798,0.0001714687,7.814194e-7,0.0007855581,0.04246738,0.8428199,0.0007700441,0.004015822,0.0102115],"study_design_scores_gemma":[0.00451173,0.0006857533,0.1657558,0.00001795944,0.0002061273,0.00003045525,0.0001676696,0.7103227,0.07327571,0.01404187,0.02980539,0.001178768],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6325198,0.00006096919,0.3669307,0.0001184605,0.0001032618,0.0001660679,0.00001932702,0.000004292058,0.00007707564],"genre_scores_gemma":[0.7968983,0.0001650333,0.2018938,0.000218691,0.0002745933,0.00001927437,0.0001006533,0.00002310442,0.0004065084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7695441,"threshold_uncertainty_score":0.4844339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03069512069075372,"score_gpt":0.29683713121111,"score_spread":0.2661420105203563,"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."}}