Cause or consequence? Exploring the role of phenotypic plasticity and genetic polymorphism in the emergence of phenotypic spatial patterns of the European eel
Bibliographic record
Abstract
The European eel (Anguilla anguilla), and generally, temperate eels, are relevant species for studying adaptive mechanisms to environmental variability because of their large distribution areas and their limited capacity of local adaptation. In this context, GenEveel, an individual-based optimization model, was developed to explore the role of adaptive phenotypic plasticity and genetic-dependent habitat selection, in the emergence of observed spatial life-history traits patterns for eels. Results suggest that an interaction of genetically and environmentally controlled growth may be the basis for genotype-dependent habitat selection, whereas plasticity plays a role in changes in life-history traits and demographic attributes. Therefore, this suggests that those mechanisms are responses to address environmental heterogeneity. Moreover, this brings new elements to explain the different life strategies of males and females. A sensitivity analysis showed that the parameters associated with the optimization of fitness and growth genotype were crucial in reproducing the spatial life-history patterns. Finally, it raises the question of the impact of anthropogenic pressures that can cause direct mortalities but also modify demographic traits and act as a selection pressure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".