Environmental stress and life-stage dependence on the detection of heterozygosity–fitness correlations in the European eel,<i>Anguilla anguilla</i>
Bibliographic record
Abstract
Heterozygosity-fitness correlations (HFCs) have been reported in populations of many species, although HFCs can clearly vary across species, conspecific populations, temporal samples, and sexes. We studied (i) the temporal stability of the association between genetic variation and growth rate (length and mass increase) and (ii) the influence of genetic variability on survival in the European eel (Anguilla anguilla L). HFCs were assessed using genotypes from 10 allozyme and 6 microsatellite markers in 22-month-old experimental individuals. The results were compared with those of a previous study carried out in 12-month-old individuals, in which more heterozygous individuals showed a significantly faster growth rate. In contrast, 22-month-old individuals showed no evidence that genetic variability was correlated with growth rate. Additionally, heterozygous individuals did not show a higher survival rate compared with more homozygous individuals after either handling stress or parasite infection. The decrease in HFCs over time is consistent with the general prediction that differences in growth and survival among individuals are maximal early in life and in our case most likely due to the relaxation of environmental conditions related to population-density effects. Alternatively, the decline in HFCs could be attributed to either ontogenetic variance in gene activity between 12- and 22-month-old individuals or differential mortality leaving only the largest individuals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".