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Record W2551919938 · doi:10.1139/gen-2016-0128

Nuclear DNA content correlates with depth, body size, and diversification rate in amphipod crustaceans from ancient Lake Baikal, Russia

2016· article· en· W2551919938 on OpenAlexafffundvenue
Nicholas W. Jeffery, Lev Y. Yampolsky, T. Ryan Gregory

Bibliographic record

VenueGenome · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaRussian Academy of SciencesNational Science Foundation
KeywordsCrustaceanBiologyAmphipodaGenome sizeEcologyGenomeHabitatGenetic algorithmZoologyGeneGenetics

Abstract

fetched live from OpenAlex

Lake Baikal in Russia is a large, ancient lake that has been the site of a major radiation of amphipod crustaceans. Nearly 400 named species are known in this single lake, and it is thought that many more await description. The size and depth of Lake Baikal, in particular, may have contributed to the radiation of endemic amphipods by providing a large number of microhabitats for species to invade and subsequently experience reproductive isolation. Here we investigate the possibility that large-scale genomic changes have also accompanied diversification in these crustaceans. Specifically, we report genome size estimates for 36 species of Baikal amphipods, and examine the relationship between genome size, body size, and the maximum depths at which the amphipods are found in the lake. Genome sizes ranged nearly 8-fold in this sample of amphipod species, from 2.15 to 16.63 pg, and there were significant, positive, phylogenetically corrected relationships between genome size, body size, maximum depth, and diversification rate among these species. Our results suggest that major genomic changes, including transposable element proliferation, have accompanied speciation that was driven by selection for differences in body size and habitat preference in Lake Baikal amphipods.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.188
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2016
Admission routes3
Has abstractyes

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