Impacts of seasonal stock mixing on the assessment of Atlantic cod in the Gulf of Maine
Bibliographic record
Abstract
Atlantic cod (Gadus morhua) in the Northwest Atlantic off New England and southern Atlantic Canada exhibit a complex population structure. This region has three independently assessed stocks [Georges Bank, Gulf of Maine (GOM), and the 4X stock], all of which are known to mix with each other. Assessments of these stocks, however, assume no interpopulation mixing. Using simulations, we evaluated impacts of ignoring mixing resulting from seasonal migrations on the GOM assessment. The dynamics of the three stocks were simulated according to different scenarios of interstock mixing, and a statistical catch-at-age stock assessment model was fitted to the simulated GOM data with and without mixing. The results suggest that, while mixing causes measurable bias in the assessment, under the conditions tested, this model still performed well. Of the bias that does exist, spawning-stock biomass estimates are relatively sensitive to mixing compared with estimates of recruitment and exploitation rate. The relative timing of seasonal migration of the three stocks plays a critical role in determining the magnitude of bias. The scale and trends among years in the bias were driven by how representative the catch and survey data were for the GOM stock; this representation changed with the mixing rates.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".