Exploring the limits of spatiotemporal and design-based index standardization under reduced survey coverage
Bibliographic record
Abstract
Abstract Indices of abundance derived from fisheries-independent surveys play a crucial role in sustainable fisheries management. While design-based methods provide unbiased indices in theory, logistical constraints may introduce biases in practice. Spatiotemporal models offer potential for mitigating such bias, but their limitations remain poorly understood. In this study, we compare the performance of model-based and design-based indices using 200 simulated populations representing two virtual species. We simulate stratified-random surveys under various scenarios, including constant effort and coverage, reduced set density, and reduced spatial coverage (e.g. closed areas). We consider three closed-area scenarios: no population recovery, population recovery, and population recovery with spillover. With consistent survey coverage, correctly specified spatiotemporal models demonstrated comparable bias, accuracy, and confidence interval coverage to design-based methods. Spatiotemporal models incorporating appropriate covariates and observation families could mitigate the impact of reduced spatial coverage. However, poorly specified models were sometimes outperformed by design-based methods. Our results, therefore, highlight the potential for spatiotemporal models to mitigate the effects of survey effort reduction on population assessment and the provision of scientific advice. However, they also present a cautionary tale about the critical importance of model evaluation and comparison.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".