An age- and sex-structured assessment model for American eels (<i>Anguilla rostrata</i>) in the Potomac River, Maryland
Bibliographic record
Abstract
American eel ( Anguilla rostrata ) and European eel ( Anguilla anguilla ) populations have declined since the 1980s prompting concern about their status and the causes of decline, but stock assessment approaches to estimate effects of fishing on these populations are lacking. Since 1964, 16% of United States commercial American eel harvest came from the Potomac River, yet American eel abundance, production, and fishing mortality is poorly understood in this system. We developed an age- and sex-structured assessment model for 1980–2008 and compared results with the F50%biological reference point (BRP). The model included natural mortality, fishing mortality, and sex- and age-specific maturation mortality and selectivity. Between 1980 and 2008 estimated recruitment, biomass, and abundance decreased 82%–89%. In all years since 1993, the exploitation rate exceeded the F50%BRP. The model was moderately sensitive to changes in natural mortality, standard deviation for fishery and recruitment catch-per-unit-effort indices, and initial fishing mortality. The multidecadal decline in recruitment in Chesapeake eels matches those reported elsewhere for American and European eels, suggesting large-scale processes have affected anguillid eel recruitment in the North Atlantic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".