{"id":"W4401660025","doi":"10.1093/evlett/qrae033","title":"The structure of the environment influences the patterns and genetics of local adaptation","year":2024,"lang":"en","type":"article","venue":"Evolution Letters","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Local adaptation; Adaptation (eye); Spatial analysis; Spatial ecology; Selection (genetic algorithm); Evolutionary biology; Genetic variation; Ecological genetics; Spatial variability; Genetic architecture; Population; Biology; Variation (astronomy); Ecology; Statistics; Computer science; Genetics; Mathematics; Machine learning; Phenotype; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001593277,0.0001979062,0.00038996,0.0004295306,0.0005141976,0.001092483,0.0004583074,0.0004387677,0.001286796],"category_scores_gemma":[0.004996582,0.0001777577,0.0004539549,0.0005957232,0.001494165,0.0007121724,0.0007501914,0.0005782425,0.0001428325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006933332,"about_ca_system_score_gemma":0.0005303301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007384019,"about_ca_topic_score_gemma":0.01565108,"domain_scores_codex":[0.9991255,0.0004286342,0.00004580314,0.0002147818,0.00007827192,0.0001069636],"domain_scores_gemma":[0.9979255,0.001074569,0.0004087909,0.0003188024,0.0001468632,0.0001254475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003109777,0.0002271673,0.7413371,0.000197544,0.0006041488,0.001128349,0.001341476,0.1461128,0.05070091,0.01614161,0.0005902736,0.04130774],"study_design_scores_gemma":[0.00004923747,0.0003819812,0.8709804,0.00005473958,0.0002576014,0.00042063,0.001608331,0.09590942,0.003550813,0.02445171,0.002244242,0.00009092573],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903566,0.0001966338,0.006950457,0.0001013293,0.000007307791,0.00001296205,0.00004557592,0.00002434326,0.002304795],"genre_scores_gemma":[0.9985373,0.0000780339,0.001181234,0.00002529131,0.000002065783,0.000005549881,0.00002153688,0.00000693128,0.0001421469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007384019,"threshold_uncertainty_score":0.01468211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005298754706344715,"score_gpt":0.1905908724105822,"score_spread":0.1852921177042375,"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."}}