{"id":"W4387475662","doi":"10.1093/molbev/msad225","title":"Comparative Population Transcriptomics Provide New Insight into the Evolutionary History and Adaptive Potential of World Ocean Krill","year":2023,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Uppsala Multidisciplinary Center for Advanced Computational Science; Horizon 2020 Framework Programme; Uppsala Universitet; Vetenskapsrådet; Centre National d’Etudes Spatiales; Centre National de la Recherche Scientifique; Flotte Océanographique Française; Université du Québec à Rimouski","keywords":"Biology; Euphausia; Krill; Adaptation (eye); Antarctic krill; Population genomics; Evolutionary biology; Genetic variation; Ecology; Population; Pelagic zone; Genomics; Gene; Genome; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004266392,0.0001890994,0.0002474234,0.0009701054,0.0003338219,0.0004457945,0.000151643,0.0002697324,0.00100472],"category_scores_gemma":[0.000519773,0.0001729435,0.0002730432,0.0008790364,0.0002545349,0.0004576077,0.0004552096,0.0003127914,0.0001704944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002976056,"about_ca_system_score_gemma":0.0002737671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002622097,"about_ca_topic_score_gemma":0.009144395,"domain_scores_codex":[0.9998354,0.00002532786,0.00001020138,0.00007619559,0.0000252703,0.0000275009],"domain_scores_gemma":[0.9997725,0.00007306124,0.0000669509,0.00002688903,0.00003553102,0.00002509405],"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":[0.0001715175,0.000040726,0.1644063,0.0003564723,0.0005350217,0.0001329451,0.0005267518,0.001385438,0.8004059,0.001168934,0.0003501189,0.03051979],"study_design_scores_gemma":[0.000006021446,0.0001010739,0.9824336,0.00002946668,0.0001197391,0.0001597085,0.0002984331,0.00242614,0.008778035,0.001392241,0.004232118,0.00002351826],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833906,0.002112865,0.007140903,0.0001016313,0.00001642454,0.00001164233,0.005389154,0.00004421912,0.001792496],"genre_scores_gemma":[0.985316,0.00122466,0.006992002,0.0001770062,0.00002084246,0.00003672966,0.005529211,0.00004258745,0.0006609501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002622097,"threshold_uncertainty_score":0.005213618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01624347743112754,"score_gpt":0.2295328776993499,"score_spread":0.2132894002682224,"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."}}