Is there a relationship between fluctuating asymmetry and reproductive investment in perch (<i>Perca fluviatilis</i>)?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".