{"id":"W4307842274","doi":"10.1002/eap.2772","title":"(Epi)genomic adaptation driven by fine geographical scale environmental heterogeneity after recent biological invasions","year":2022,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Marine Ecology and Invasive Species","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Youth Innovation Promotion Association of the Chinese Academy of Sciences; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Biology; Epigenetics; Local adaptation; Adaptation (eye); DNA methylation; Evolutionary biology; Genetic variation; Genetics; Epigenomics; Gene; Population; Gene expression; Neuroscience","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":[0.0002193634,0.0001342033,0.0002739321,0.0004902768,0.0002080042,0.0003574333,0.0001995207,0.0002615931,0.001104456],"category_scores_gemma":[0.0005827531,0.0001053017,0.0002216426,0.0004910874,0.0003817052,0.0002536993,0.000508346,0.0003581745,0.0001502548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001910059,"about_ca_system_score_gemma":0.0001306317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001900841,"about_ca_topic_score_gemma":0.003829041,"domain_scores_codex":[0.9998363,0.00002037309,0.00000876039,0.00008105835,0.00002232596,0.00003120412],"domain_scores_gemma":[0.9997434,0.00004518701,0.00008993361,0.00003133982,0.00005194837,0.0000380902],"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":[0.000332461,0.00002881844,0.1729748,0.0001283319,0.0001788011,0.0003943259,0.0008917594,0.001145414,0.8057875,0.0008399059,0.0002502507,0.01704768],"study_design_scores_gemma":[0.000003223995,0.00005134304,0.9938929,0.000004595945,0.00003446108,0.0001392058,0.0001964827,0.0006905529,0.003950357,0.0002885991,0.0007378826,0.00001038074],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968747,0.0003262895,0.001700066,0.00003569838,0.000006776419,0.000006735982,0.0003276109,0.00002741167,0.0006947644],"genre_scores_gemma":[0.9985074,0.0001027985,0.0005723246,0.00005714385,0.00000609396,0.00001269794,0.0003591566,0.00001041045,0.0003718061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001900841,"threshold_uncertainty_score":0.00377959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235465646391461,"score_gpt":0.2063443066363542,"score_spread":0.1839896501724396,"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."}}