{"id":"W3174733714","doi":"10.1101/2020.12.28.424584","title":"Locally adaptive inversions modulate genetic variation at different geographic scales in a seaweed fly","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill Genome Centre; Université Laval","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Vetenskapsrådet; Genome Canada; Universitetet i Oslo; McGill University; Norges Forskningsråd; Norwegian Sequencing Centre; Fonds de recherche du Québec – Nature et technologies; Université Laval","keywords":"Local adaptation; Cline (biology); Biology; Adaptation (eye); Evolutionary biology; Range (aeronautics); Gene flow; Hybrid zone; Linkage disequilibrium; Genetic variation; Ecology; Genetics; Gene; Haplotype; Population; Allele","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":[0.0001283972,0.0001214088,0.0001724972,0.0005334012,0.0001862812,0.0003250473,0.0001250015,0.0001996098,0.0009409465],"category_scores_gemma":[0.0003588454,0.0001193625,0.0001583006,0.0003915131,0.0002393626,0.0001393532,0.0002772342,0.0002779239,0.0001486666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002448946,"about_ca_system_score_gemma":0.0001261324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004048981,"about_ca_topic_score_gemma":0.008756564,"domain_scores_codex":[0.999916,0.00001130154,0.000004776367,0.00003555666,0.00001548888,0.00001696018],"domain_scores_gemma":[0.9998393,0.0000496973,0.00005053035,0.00001765639,0.00001957989,0.00002327201],"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":[0.000169408,0.00002891145,0.1236345,0.00006105286,0.00009676316,0.0001383434,0.0004284103,0.001762346,0.8645569,0.0005136587,0.000155019,0.008454609],"study_design_scores_gemma":[0.0000115003,0.00004337211,0.9765785,0.00001115018,0.00004315953,0.0001328794,0.0002720804,0.003668051,0.01790132,0.000305872,0.001018152,0.00001401752],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985268,0.00005193867,0.000910654,0.00001101538,8.956839e-7,0.000001969589,0.0002656729,0.00001652158,0.0002144902],"genre_scores_gemma":[0.9982896,0.00003893448,0.0009313342,0.00001908284,0.000001606131,0.00000304877,0.0005043017,0.00001394485,0.0001981859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004048981,"threshold_uncertainty_score":0.008050799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01291815645518901,"score_gpt":0.1970954874410143,"score_spread":0.1841773309858253,"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."}}