Testing the performance of a spatially explicit tag-integrated stock assessment model of yellowtail flounder (<i>Limanda ferruginea</i>) through simulation analysis
Bibliographic record
Abstract
In any stock assessment application, the implicit assumptions regarding spatial population structure must be carefully evaluated. Tag-integrated models offer a promising approach for incorporating spatial structure and movement patterns in stock assessments, but the complexity of the framework makes implementation challenging and the appraisal of performance difficult. A flounder-like fishery was simulated to emulate the metapopulation dynamics of the three yellowtail flounder (Limanda ferruginea) stocks off New England, and the robustness of spatially explicit tag-integrated models were compared with closed population assessments. Different movement parametrizations and data uncertainty scenarios were simulated, while the ability of the tag-integrated model to estimate reporting rate and time-varying movement were also evaluated. Results indicated that the tag-integrated framework was robust for the simulated fishery across a wide range of connectivity levels and that tag reporting rates were accurately estimated. Closed population models also demonstrated limited error. Therefore, spatially explicit approaches may not always be warranted even when regional connectivity is occurring, but tag-integrated models can provide improved parameter estimates when reliable tagging data are available. Tag-integrated models also serve as valuable tools for informing spatially explicit operating models, which can then be used to evaluate the assumptions and performance of closed population 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".