{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005775793,0.0001955861,0.0002836136,0.0008042511,0.0003496622,0.0005475936,0.0002800401,0.0002985101,0.001471052],"category_scores_gemma":[0.0005461068,0.000150306,0.0004280862,0.0007424654,0.0001982469,0.0004496786,0.000485784,0.0004834394,0.0002202615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002030224,"about_ca_system_score_gemma":0.0001674416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002184569,"about_ca_topic_score_gemma":0.006055679,"domain_scores_codex":[0.9998342,0.00003536485,0.000008854698,0.00006985933,0.00002455893,0.00002723491],"domain_scores_gemma":[0.9996531,0.00009518598,0.0001108502,0.00004225327,0.00004947485,0.00004914627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002202755,0.0001580892,0.6224508,0.0002033767,0.0007422601,0.0003073581,0.0009711977,0.005842569,0.3206297,0.001270221,0.0007215058,0.04648258],"study_design_scores_gemma":[0.000007412522,0.00007181733,0.9887325,0.00002153608,0.00008679023,0.0001795057,0.0004119824,0.005278222,0.002849976,0.0007665126,0.001579761,0.00001404379],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990703,0.0002700839,0.007152369,0.00006977668,0.000008987799,0.00001244976,0.0008141445,0.00005875253,0.0009104618],"genre_scores_gemma":[0.9932479,0.00008550196,0.005462204,0.00006874515,0.00001002704,0.00001619551,0.0008003905,0.00001375968,0.0002952113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002184569,"threshold_uncertainty_score":0.004921138,"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."}}