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Record W2759323351 · doi:10.1111/eff.12380

Phenotypic plasticity in the morphology of small benthic Icelandic Arctic charr (<i>Salvelinus alpinus</i>)

2017· article· en· W2759323351 on OpenAlexfundno aff
Bjarni K. Kristjánsson, Camille A. Leblanc, Skúli Skúlason, Sigurður S. Snorrason, David L. G. Noakes

Bibliographic record

VenueEcology Of Freshwater Fish · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaIcelandic Centre for Research
KeywordsPhenotypic plasticityBiologySalvelinusIntraspecific competitionEcologyBiodiversityBenthic zoneEvolutionary biologyFish <Actinopterygii>TroutFishery

Abstract

fetched live from OpenAlex

Abstract Intraspecific phenotypic diversity is the raw material for evolution, so understanding its origin and maintenance is critically important for conservation of biodiversity. Intraspecific diversity in a trait or a suite of traits can result from genetic diversity and/or phenotypic plasticity. The two are, however, not independent as plasticity has been shown to evolve. In this study, we evaluated the importance of phenotypic plasticity in generating morphological diversity in populations of small benthic Arctic charr in Iceland, using a rearing experiment with contrasting modes of feeding. We also examined the association between phenotypic plasticity in offspring groups generated by the contrasting feeding modes and important ecological variables characterising the natural habitats of the respective populations. Although the level of plasticity could not be related to any of the ecological measurements, clear differences in morphological reaction norms among populations suggest that plasticity is an important aspect of morphological diversity of the charr. It is not clear whether that plasticity is adaptive, but it is notable that reaction norms in all populations have similar reaction to the treatments.

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 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 score0.490

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 science0.0010.000
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 head0.017
GPT teacher head0.230
Teacher spread0.214 · 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

Citations17
Published2017
Admission routes1
Has abstractyes

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