The evolving story of catadromy in the European eel (<i>Anguilla anguilla</i>)
Bibliographic record
Abstract
Abstract Anguillid eels were once considered to be the classic example of catadromy. However, alternative life cycles have been reported, including skipping the freshwater phase and habitat shifting between fresh, brackish, and saltwater throughout the growth phase. There is a lack of knowledge regarding these alternate life strategies, for example, the proportion of individuals in the population that adopt them compared to classic catadromy. We provide a description of these alternate life cycle strategies in temperate anguillids, their possible drivers, and the methods available to investigate them. These methods (lethal and non-lethal), include otolith microchemistry, fatty acid and stable isotope analyses, parasite identification, blood transcriptomics, and electronic tags. We argue that since the current management framework for the European eel and other temperate eels is based mainly on the freshwater component of the population, it ignores eels growing in saline waters. Many of the factors that are thought to be responsible for the precipitous decline of the eel population are more prevalent in freshwater systems. Therefore, the contribution of saline eels may be more important than currently estimated. The habitat-shifting ability of eels may be all the more crucial for the persistence and recovery of those species that are endangered.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".