{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002260719,0.0001978115,0.0002165453,0.0000379368,0.0009606549,0.00001777722,0.0004545607,0.0001380908,0.0969966],"category_scores_gemma":[0.00002539683,0.000173514,0.0001307167,0.0002948366,0.0006393291,0.00007739502,0.001282083,0.0004137788,0.001272359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003491167,"about_ca_system_score_gemma":0.00001049733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001836196,"about_ca_topic_score_gemma":0.0003184557,"domain_scores_codex":[0.9981133,0.000237207,0.0003437169,0.0006751943,0.0002313327,0.0003992674],"domain_scores_gemma":[0.9992056,0.0001603287,0.0001119436,0.0003333676,0.000004430945,0.0001843001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005907413,0.001907263,0.9630132,0.000001163371,0.0000223209,0.000005272933,0.00005983939,0.00128351,0.01866421,0.00029448,0.009635015,0.005054666],"study_design_scores_gemma":[0.0002235034,0.0002861407,0.7649923,2.195656e-7,0.00001433669,0.00001408518,0.00008330047,0.0002352434,0.0003532385,0.001347219,0.2322434,0.0002070064],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933519,0.00007876666,0.0005717317,0.002137191,0.00006871398,0.0008806654,0.0002924331,0.00008709174,0.002531514],"genre_scores_gemma":[0.9912986,0.0002140156,0.001293555,0.001436355,0.00004274044,0.004800519,0.0005263568,0.00001134093,0.0003765423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2226084,"threshold_uncertainty_score":0.9995053,"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."}}