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Record W1982841323 · doi:10.1139/z01-215

Is there a relationship between fluctuating asymmetry and reproductive investment in perch (<i>Perca fluviatilis</i>)?

2002· article· en· W1982841323 on OpenAlexvenueno aff
Sigurd Øxnevad, Erik Heibo, Leif Asbjørn Vøllestad

Bibliographic record

VenueCanadian Journal of Zoology · 2002
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsnot available
FundersNorges Forskningsråd
KeywordsFluctuating asymmetryPerchBiologyMeristicsFish finGonadosomatic IndexPopulationZoologyFecundityDorsal finEcologyFisheryFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

Fluctuating asymmetry (FA), or random deviation from perfect bilateral symmetry, is often used as an indicator of perturbed development. Several studies attempt to correlate FA with components of individual fitness or population viability. In this study we test for a correlation between FA and four fitness traits in female Eurasian perch (Perca fluviatilis) inhabiting acidified or non-acidified lakes. Three bilateral meristic characters were counted on each side of the fish: number of gill rakers on the lower first branchial arch, number of gill rakers on the upper first branchial arch, and number of pectoral-fin rays. An asymmetry index summarizing the numbers of asymmetric characters per fish was also calculated. Four traits related to fitness were measured: gonad dry mass, egg mass, gonadosomatic index, and fecundity. There were significant differences in FA among the five perch populations for the characters number of pectoral-fin rays and number of upper gill rakers, and also for the FA index. Asymmetry was generally greater in perch living in acidified lakes than in those in non-acidified lakes. However, there was no significant correlation between FA and any of the four fitness-related traits within populations. Therefore, asymmetry in the traits measured here may not be a good indicator of individual fitness in perch.

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)0.003
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 score0.084
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
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 head0.103
GPT teacher head0.296
Teacher spread0.192 · 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

Citations32
Published2002
Admission routes1
Has abstractyes

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