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Record W1973282440 · doi:10.1139/z08-010

Effect of environmental stress on fluctuating asymmetry in certain morphological traits in Drosophila ananassae: nutrition and larval crowding

2008· article· en· W1973282440 on OpenAlexvenueno aff
C. Vishalakshi, B. N. Singh

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsnot available
Fundersnot available
KeywordsFluctuating asymmetryBiologyTraitAsymmetryLarvaEnvironmental stressDrosophila (subgenus)ZoologyPhenotypeEvolutionary biologyEcologyGeneticsGene

Abstract

fetched live from OpenAlex

The association of fluctuating asymmetry and phenotypic and genetic variability with environmental stress was investigated using poor nutrition and larval density as stresses on 10 recently collected isofemale lines of Drosophila ananassae Doleschall, 1858. Trait means for different morphological traits were reduced by stress, whereas phenotypic and genetic variability increased. The levels of fluctuating asymmetry and positional fluctuating asymmetry were similar in flies reared on poor and standard media. In constrast, there is a significant difference in both asymmetry measures in the flies reared at different larval densities for all traits. However, when asymmetry values across all traits were combined into a single index, composite fluctuating asymmetry, significant differences were found in males and females reared at different larval densities but not under nutritional stress. Moreover, composite fluctuating asymmetry is higher in males than in females, suggesting that males are more vulnerable to developmental stress. The results suggest that trait means are more sensitive to stress than fluctuating-symmetry measures and that the effect of stress is trait- and sex-specific. Generalizations based on the use of fluctuating asymmetry as an indicator of environmental stress in D. ananassae should therefore be used with caution.

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)0.001
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 score0.019
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.024
GPT teacher head0.259
Teacher spread0.235 · 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

Citations28
Published2008
Admission routes1
Has abstractyes

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