Explicit incorporation of spatial variability in a biomass dynamics assessment model
Bibliographic record
Abstract
Abstract The sustainable management of fisheries has largely relied on stock assessment models that assume stocks are homogeneous throughout their domain. However, ignoring complex underlying spatial patterns can lead to increased risk of failures in management. Utilizing geostatistical approaches in conjunction with a traditional fishery biomass dynamics model, we develop a spatially-explicit modelling framework that treats the underlying population dynamics as spatial processes. Simulation experiments demonstrate that this approach reliably estimates variance parameters and accurately captures true patterns of population change. We further demonstrate the utility of our modelling framework in a real setting using data from the Canadian Maritimes Inshore Scallop Fishery. The model captures time-varying spatial patterns in both population characteristics and fishing pressure without explicit knowledge of the underlying mechanisms and retains the ability to scale up to the whole spatial domain with less associated uncertainty than for temporal models. These results lead to improved scientific advice for management, future-proofing of the assessment to shifts in stock productivity and fishing effort, and provide information that can be used to develop more effective management approaches.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".