Estimating Stock Composition of Anadromous Fishes from Mark–Recovery Data: Possible Application to American Shad
Bibliographic record
Abstract
Abstract Information on the stock composition of mixed-stock fisheries is often needed to develop management regulations for anadromous fishes. Although several methods can be used to infer stock composition, marking studies have long been identified as a promising approach. Hatchery-reared larval American shad Alosa sapidissima are marked with a river-specific mark and released in stock enhancement programs along the U.S. East Coast. We describe and apply a mark–recovery method for inferring the proportion of the catch in a mixed-stock fishery that originates from a particular river. The method is based on comparing the proportion of the mixed-stock catch with marks from the river with the proportion of the fish returning to the river with marks. We explore the utility of using mass marking of hatchery-reared American shad larvae with tetracycline to determine the stock composition of mixed-stock fisheries of American shad in Virginia. Our analysis focuses on the impact of the former Virginia coastal ocean fishery on fish produced in the James and Pamunkey rivers, Virginia, and on the impact of bycatch in Chesapeake Bay pound nets on Susquehanna River American shad. Our results suggest that the coastal ocean fishery harvested relatively small proportions of the James and Pamunkey River stocks and that few American shad captured in pound nets sampled in Chesapeake Bay were from the Susquehanna River system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".