Extreme variability in European eel growth revealed by an extended mark and recapture experiment in southern France and implications for management
Bibliographic record
Abstract
The European eel (Anguilla anguilla) is endangered due to its peculiar life-history cycle, fishing pressure and difficulty in global population management. To improve our understanding of the population dynamics and refine conservation policies, an extended mark and recapture experiment and glass eel stocking were conducted in the River Rhône Delta (France) over 8 years. Around 1100 yellow eels were PIT-tagged and released in 2007, 2.5 kg of glass eels were released each year from 2008 to 2012, and the population was monitored using fishing between 2007 and 2015. After capture or recapture, the body parameters, sex and maturity were assessed. Age was estimated from otolith growth rings. At the end of the experiment, silver eels were between 352 and 875 mm long (age 17 to 185 months) and yellow eels between 170 and 868 mm long (age 12 to 123 months). Age estimates were validated using mark and recapture and showed 16% age underestimates and 5% inaccurate ages. The growth rates were extremely variable with lengths ranged by 2-fold at a given age. These results highlight the difficulty of eel population management, at least in the Mediterranean area.
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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 | 0.001 |
| 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) | 0.000 | 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".