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Record W2135965538 · doi:10.1111/evo.12442

PHENOTYPE-ENVIRONMENT ASSOCIATION OF THE OXYGEN TRANSPORT SYSTEM IN TRIMORPHIC EUROPEAN WHITEFISH (<i>Coregonus lavaretus</i>) POPULATIONS

2014· article· en· W2135965538 on OpenAlexafffund
Melissa L. Evans, Kim Præbel, Stefano Peruzzi, Per‐Arne Amundsen, Louis Bernatchez

Bibliographic record

VenueEvolution · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaNorges Forskningsråd
KeywordsBiologyCoregonus lavaretusProfundal zonePelagic zoneCoregonusEcologyLittoral zoneEvolutionary biologyZoologyFishery

Abstract

fetched live from OpenAlex

Replicated adaptive radiation events, typified by phenotypic divergence across resource axes, provide important insight into the eco-evolutionary dynamics that lead to the formation of new species. Here, we show that in trimorphic adaptive radiations of European whitefish (Coregonus lavaretus), divergence of the oxygen transport system has occurred across the pelagic/littoral (shallow)-profundal (deep) resource axis, and at multiple biological scales. Profundal whitefish exhibited significantly larger red blood cells (RBCs), a greater proportion of cathodic hemoglobin protein components, and higher hemoglobin transcript abundance in kidney compared to littoral and pelagic morphs. Hemoglobin transcript abundance in brain and gill, but not kidney, and anodic hemoglobin protein component diversity in blood were also linked to variation at an intronic single nucleotide polymorphism (SNP). As the whitefish morphs differ in population genetic structure at this SNP, hemoglobin transcript and protein divergence between profundal and pelagic/littoral morphs is likely being driven by genetic divergence. Our findings, along with our previous work on lake whitefish, highlight the importance of the oxygen transport system to the postglacial colonization of novel lacustrine environments by whitefish throughout the northern hemisphere.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.006
GPT teacher head0.168
Teacher spread0.162 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2014
Admission routes2
Has abstractyes

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