Demographics and Parasitism of American Eels in the Chesapeake Bay, USA
Bibliographic record
Abstract
Abstract The Chesapeake Bay supports the largest U.S. harvest of American eels Anguilla rostrata, yet little is known about the underlying demographics and production rates that sustain these harvests. Chesapeake Bay subestuaries (Sassafras, Chester, Choptank, Patuxent, Potomac, and James rivers) are expected to provide productive but varying growth habitats for yellow‐stage American eels due to differences in land use, prey availability, and salinity. We compared length, age, growth, mortality, condition, and health (prevalence and incidence of parasitism by the swim bladder nematode Anguillicola crassus) of yellow American eels (n = 850) in the six principal subestuaries. As has been observed for other systems, female American eels in the Chesapeake Bay were larger, older, and heavier and had higher growth rates than male, intersexual, or undifferentiated American eels. Prevalence of male and intersexual individuals in the upper bay region was higher than that in the lower bay and higher than that reported for estuaries in South Carolina, Quebec, and the Hudson River, New York. American eel demographic attributes (gender, length, weight, condition, age, growth, and parasitism) differed substantially among the subestuaries. Individual growth rates ranged from 26.7 to 149.3 mm/year; the Choptank River had the highest mean growth rate (72.7 mm/year), and the Chester River had the lowest (60.2 mm/year). Estimated instantaneous loss rates (0.52–1.01 per year) did not vary systematically among subestuaries. Prevalence of parasitized American eels ranged from 17.8% to 72.0% and was higher in the upper bay subestuaries than in the lower bay. Swim bladder damage and Anguillicola crassus presence were not associated with American eel age or growth rate. In summary, female prevalence, growth rates, and condition were lower and parasite prevalence and intensity were higher in the less‐saline upper bay than in the lower bay, suggesting fundamental differences in the productivity and spawning contributions between these two regions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".