{"id":"W2806040017","doi":"10.1101/337543","title":"Broad geographic sampling reveals predictable, pervasive, and strong seasonal adaptation in <i>Drosophila</i>","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; European Commission; National Institutes of Health; National Evolutionary Synthesis Center","keywords":"Adaptation (eye); Seasonality; Allele frequency; Temperate climate; Allele; Selection (genetic algorithm); Natural selection; Genetic variation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002342792,0.0000987259,0.0001772335,0.0005139092,0.0001449975,0.0002938672,0.0001535939,0.0001568147,0.0007046417],"category_scores_gemma":[0.0003348301,0.0001127536,0.0001282401,0.0004312145,0.0002771615,0.0001200458,0.0002442242,0.0002259052,0.0001308919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001807768,"about_ca_system_score_gemma":0.00009041203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002356636,"about_ca_topic_score_gemma":0.005279283,"domain_scores_codex":[0.9998611,0.00001821539,0.000008100229,0.00007390336,0.00001911047,0.00001951037],"domain_scores_gemma":[0.9997354,0.0000459653,0.0001168684,0.00002986171,0.00003042624,0.00004140199],"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":[0.0001861733,0.00002345371,0.2142294,0.0000532532,0.0001531134,0.0001034524,0.0002492658,0.0004902655,0.7744735,0.0002482113,0.000190127,0.009599661],"study_design_scores_gemma":[0.00000311519,0.00003295285,0.9930359,0.000003007799,0.00001986996,0.0001369287,0.00007232284,0.0004403998,0.005852215,0.00006869176,0.0003294049,0.000005243228],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990023,0.00007305198,0.0004831766,0.00001160732,8.241103e-7,0.00000150098,0.0001813388,0.00001210712,0.0002340802],"genre_scores_gemma":[0.9993333,0.00003170994,0.0002957534,0.00002275225,0.000001637295,0.000002033928,0.0002355255,0.000006462424,0.00007071494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002356636,"threshold_uncertainty_score":0.004685879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01768008076477265,"score_gpt":0.2239398140726293,"score_spread":0.2062597333078566,"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."}}