{"id":"W1624362487","doi":"10.1007/s00239-015-9696-6","title":"Genomics of Adaptation to Multiple Concurrent Stresses: Insights from Comparative Transcriptomics of a Cichlid Fish from One of Earth’s Most Extreme Environments, the Hypersaline Soda Lake Magadi in Kenya, East Africa","year":2015,"lang":"en","type":"article","venue":"Journal of Molecular Evolution","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Alexander von Humboldt-Stiftung; Universität Konstanz; European Molecular Biology Organization","keywords":"Biology; Cichlid; Transcriptome; Extremophile; Extreme environment; Adaptation (eye); Tilapia; Gill; Freshwater fish; Zoology; Ecology; Gene; Fish <Actinopterygii>; Genetics; Fishery; Gene expression","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001069338,0.0001096437,0.0003018158,0.00004062159,0.00001927284,0.000004293402,0.0002207915,0.00006256765,0.0000441152],"category_scores_gemma":[0.00008597273,0.0000805395,0.00008877882,0.0001969308,0.0001748893,0.0001088066,0.0000592523,0.0001389743,0.000003754802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001033553,"about_ca_system_score_gemma":0.00003354354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005136784,"about_ca_topic_score_gemma":0.00109614,"domain_scores_codex":[0.9986104,0.000139319,0.0005933574,0.000144839,0.0004055094,0.0001065936],"domain_scores_gemma":[0.9991811,0.00007624613,0.0004667429,0.0001288475,0.00004690063,0.0001001876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002351775,0.0004814781,0.001220785,0.000002742872,0.00003858392,0.000001477842,0.002660459,0.04467302,0.9502633,0.00002168576,0.0000553658,0.0003459511],"study_design_scores_gemma":[0.003208158,0.0009648275,0.3264503,0.000211538,0.0001771568,0.000001793848,0.005659579,0.03773838,0.6197152,0.003831217,0.001748132,0.0002936555],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751611,0.0004099729,0.02375528,0.0001474032,0.00005063811,0.0001781937,0.0002446497,0.000001521832,0.00005120353],"genre_scores_gemma":[0.9957279,0.00003416619,0.004115918,0.0000315761,0.00002094954,0.000002937371,0.00005725178,0.000004840449,0.000004445511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.330548,"threshold_uncertainty_score":0.3284305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06277612110933566,"score_gpt":0.2215324341778244,"score_spread":0.1587563130684888,"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."}}