Exploring the population dynamics of winter skate (Leucoraja ocellata) in the Georges Bank region using a statistical catch-at-age model incorporating length, migration, and recruitment process errors
Bibliographic record
Abstract
Winter skate ( Leucoraja ocellata ) of all length classes increased dramatically in abundance on Georges Bank in the 1980s following the decline of many groundfish species. We present a full population model of winter skate to better understand the population dynamics of the species and elucidate the mechanisms underlying their increase in abundance in the 1980s. Specifically, we developed four statistical catch-at-age models incorporating length-frequency data with the following model structures: (i) observation error only (base model R1); (ii) observation and recruitment process errors (model R2); (iii) adult migration modeled as a random walk in adult mortality (model R3); and (iv) observation and recruitment process errors and adult migration (model R4). Akaike’s information criterion values indicated that models R3 and R4, which both included adult migration, were the most parsimonious models. This finding strongly suggests that the winter skate population increase on Georges Bank in the 1980s was not solely a result of increases in recruitment but likely involved adult migration (i.e., it is an open population). Finally, recent predicted fishing mortalities exceeded FMSY for all models.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".