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Record W2782524826 · doi:10.1002/ece3.3761

Variation in the magnitude of morphological and dietary differences between individuals among populations of small benthic Arctic charr in relation to ecological factors

2018· article· en· W2782524826 on OpenAlexfundno aff
Bjarni K. Kristjánsson, Camille A. Leblanc

Bibliographic record

VenueEcology and Evolution · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsnot available
FundersIcelandic Centre for ResearchUniversity of Guelph
KeywordsBenthic zoneBiologyArcticEcologyVariation (astronomy)

Abstract

fetched live from OpenAlex

The early stages of intraspecific diversity are important for the evolution of diversification and speciation. Early stages of diversification can be seen in individual specialization, where individuals consume only a portion of the diet of the population as a whole, and how such specialization is related to phenotypic diversity within populations. Here, we study the strength of the relationship between morphological and dietary distances among individuals in eighteen populations of Icelandic small benthic charr. We furthermore studied if the strength of the relationship could be related to variation in local ecological factors these populations inhabit. In all the populations studied, there was a clear relationship between morphological and dietary distances, indicating that fish that had similar morphology were at the same time-consuming similar food items. Our findings show a systematic variation in the relationship between morphology and diet at early stages of diversification in a highly specialized small benthic charr morph. The results show the importance of fine scale comparisons within populations and furthermore the value that systematic comparisons among populations under parallel evolution can contribute toward our increased understanding of evolutionary and ecological processes.

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 score0.006
Threshold uncertainty score0.301

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 head0.078
GPT teacher head0.254
Teacher spread0.177 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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