Characterizing Uncertainty in Fish Stock Assessments: the Case of the Southern New England–Mid‐Atlantic Winter Flounder
Bibliographic record
Abstract
Abstract The reauthorization of the Magnuson–Stevens Act requires specification of scientific uncertainty associated with stock assessments. The scientific uncertainty associated with stock assessments of southern New England–mid‐Atlantic winter flounderPseudopleuronectes americanusis considered as a case study. Focus is placed upon the uncertainties associated with the assumptions, assertions, and choices (AACs) made in the stock assessment analysis. Two classes of AACs are discussed. The first class involves AACs that characterize the population dynamics of the stock; these AACs include the unit stock assumption, the problem of dealing with retrospective patterns, the method of averaging fishing mortality across cohorts to yield an annual value for fishing mortality, and the equilibrium structure of the stock. The second class of AACs is related to the choice of methods used to determine whether the stock is overfished; these AACs involve focusing on the maximum sustainable yield (MSY) proxy rather than MSY per se to determine overfishing levels. The MSY proxy approach is discussed and compared with heuristic calculations of the MSY approach. Arbitrary choices of instantaneous natural mortality and percent maximum spawning potential in the analysis can lead to an arbitrary decision on whether or not the stock is overfished. We conclude that there is considerable scientific uncertainty on the status of the southern New England–mid‐Atlantic winter flounder stock. The uncertainty identifies critical unknowns in winter flounder population dynamics.
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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".