{"id":"W2953112128","doi":"10.1186/s13059-018-1545-7","title":"Modularity of genes involved in local adaptation to climate despite physical linkage","year":2018,"lang":"en","type":"article","venue":"Genome biology","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"College of Agriculture and Life Sciences, Virginia Polytechnic Institute and State University; Division of Environmental Biology; Genome Alberta; University of British Columbia; Alberta Innovates Bio Solutions; Ministry of Forests, Lands and Natural Resource Operations; Genome British Columbia; Alberta Innovates; Forest Genetics Council of British Columbia; Genome Canada; National Science Foundation","keywords":"Biology; Pleiotropy; Local adaptation; Pinus contorta; Adaptation (eye); Genetic architecture; Modularity (biology); Genetics; Selection (genetic algorithm); Evolutionary biology; Natural selection; Linkage (software); Balancing selection; Gene; Candidate gene; Computational biology; Allele; Quantitative trait locus; Ecology; Population; Phenotype","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":[0.00009983207,0.00009232989,0.0001408579,0.0000621174,0.00003702504,0.000003550541,0.0001371025,0.0001583235,0.00001890229],"category_scores_gemma":[0.00001823514,0.00009141866,0.00004465148,0.0001082881,0.0001381703,0.000001833075,0.0001406926,0.00004399087,0.00002424327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009158906,"about_ca_system_score_gemma":0.00002532661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008248293,"about_ca_topic_score_gemma":0.0003155714,"domain_scores_codex":[0.9993035,0.00006774774,0.0001455791,0.0002507289,0.00004339775,0.00018903],"domain_scores_gemma":[0.9996106,0.000005025708,0.00005377916,0.0001903019,0.00008768653,0.00005263293],"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":[0.0001497323,0.0000396769,0.03796485,0.00001422135,0.00001993876,7.090229e-7,0.0005620049,0.001626151,0.9509993,0.0004234795,0.00001476151,0.008185133],"study_design_scores_gemma":[0.001559456,0.002479346,0.6470268,0.00001395457,0.00003568884,0.000005245766,0.0007616776,0.002647348,0.3049724,0.002265175,0.03766287,0.0005700444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834256,0.000145247,0.01596638,0.00004818767,0.000117774,0.0001192608,0.0000483827,0.000004905957,0.0001242527],"genre_scores_gemma":[0.9971224,0.0000342881,0.002193427,0.0001967189,0.0002247928,0.000003813119,0.000197368,0.000005960643,0.00002123113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.646027,"threshold_uncertainty_score":0.3727945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01949774506673401,"score_gpt":0.2613186195745796,"score_spread":0.2418208745078456,"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."}}