Application of Generalized Depletion Model to Recruitment of American Eel Elvers and Empirical Support from Survey Data
Bibliographic record
Abstract
Abstract Recruitment is one of the driving forces determining the population dynamics of a stock, and knowledge about recruitment is crucial in providing reliable results in modern stock assessment. The population of the American Eel Anguilla rostra is at risk, and the fisheries on the elvers are one threat for its persistence. We applied a generalized depletion model on the catch and effort data of the elver fishery of the East River, Chester, Nova Scotia, from 1996 to 2018. The elver fishery of the East River did not have high exploitation pressure on the elver waves passing through. The natural mortality rate varied substantially among years, with a mean generally in agreement with other studies. The estimates of the model parameters did not exhibit significant temporal trends. Moreover, the estimate of annual elver abundance matched well with the fishery‐independent abundance index, providing the first empirical support of the generalized depletion model. The generalized depletion model is an effective method to assess the exploitation status of elver fisheries, as well as providing an estimate of recruitment strength and natural mortality that can be used for stock assessment on the adult stocks when only fisheries‐dependent catch and effort data are available.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".