{"id":"W4391379685","doi":"10.1101/2024.01.29.577252","title":"Diving into broad-scale and high-resolution population genomics to decipher drivers of structure and climatic vulnerability in a marine invertebrate","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Ocean Acidification Effects and Responses","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Université du Québec à Rimouski; Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; Government of Canada","keywords":"DECIPHER; Vulnerability (computing); Scale (ratio); Population genomics; Invertebrate; Population; Genomics; Geography; Ecology; Marine invertebrates; Biology; Data science; Environmental resource management; Environmental science; Computer science; Cartography; Genome; Bioinformatics; Computer security; Environmental health; Medicine; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.0005268665,0.0002531654,0.0003629553,0.0002981971,0.00008859867,0.0001313401,0.0001453387,0.0002357896,0.00006712238],"category_scores_gemma":[0.0001710982,0.0002290918,0.0000337333,0.0003422695,0.00009955775,0.0001048138,0.0002113445,0.0003483592,0.000006964621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005454692,"about_ca_system_score_gemma":0.0001000517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003648699,"about_ca_topic_score_gemma":0.001771834,"domain_scores_codex":[0.9984137,0.0001966564,0.0003978469,0.0005898277,0.0001828087,0.0002191273],"domain_scores_gemma":[0.9991234,0.0001064657,0.0001679704,0.0003522399,0.00008625845,0.0001636498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006355197,0.00001559615,0.9507158,0.0009521545,0.00002970344,0.000005199101,0.000105828,0.003921149,0.0439082,0.00006647372,0.000011615,0.0002047606],"study_design_scores_gemma":[0.0001668755,0.00004233355,0.966272,0.0001985961,0.00003981009,2.092033e-8,0.000007348252,0.02845557,0.004422635,0.000123086,0.00002459413,0.0002471424],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980957,0.0004796503,0.00009477105,0.0001733565,0.0003649952,0.0005200996,0.0002209773,0.00004870836,0.000001721493],"genre_scores_gemma":[0.992678,0.0001111747,0.007062111,0.00006385243,0.0000619287,0.000003918472,0.000002996417,0.00001448541,0.000001473699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03948556,"threshold_uncertainty_score":0.9342093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007243836311972312,"score_gpt":0.2021901046917903,"score_spread":0.194946268379818,"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."}}