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Record W2096146080

Genetic variation and phenotypic plasticity: Causes of morphological and dietary variation in Eurasian perch

2006· article· en· W2096146080 on OpenAlexaff
Richard Svanbäck, Peter Eklöv

Bibliographic record

VenueEvolutionary ecology research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPelagic zonePerchLittoral zoneBiologyPhenotypic plasticityEcologyOffspringPercidaePredationZoologyHabitatFisheryFish <Actinopterygii>
DOInot available

Abstract

fetched live from OpenAlex

Question: What is the importance of genetic variation and phenotypic plasticity in forming the morphological difference between littoral and pelagic perch? Organism: Juveniles of Eurasian perch (Perca fluviatilis L.). Site: Enclosures (2 × 2 m) in a pond, Robacksdalen, Umea, Sweden. Methods: Adults from the littoral and pelagic habitats were bred separately and their offspring were raised in enclosures with either open water or vegetation in an artificial pond. Results: Offspring from littoral parents had a higher proportion of littoral prey types in their diet than pelagic offspring even though there were no differences in prey community between treatments. Littoral offspring had a deeper body than pelagic offspring raised in the same environment. However, most of the phenotypic variation in this experiment was explained by phenotypic plasticity: offspring from both parental types raised in open water displayed pelagic-type characteristics, whereas offspring raised in vegetation displayed littoral-type characteristics. Conclusion: Previous long-term studies on perch show that they experience a fluctuating environment due to population dynamics. The plasticity in perch could therefore be important as fluctuating environments favour plasticity.

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 score0.012
Threshold uncertainty score0.693

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.275
Teacher spread0.249 · 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

Citations90
Published2006
Admission routes1
Has abstractyes

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