The sensitivity of fisheries-independent survey indices to decisions of sampling design and intensity and the mitigation of biased precision estimators for systematic sampling
Bibliographic record
Abstract
Fisheries-independent surveys are used to track trends in fish stocks globally. Survey alterations occur for a myriad of reasons (e.g., vessel availability, changes in fish distribution, funding shortfalls). Understanding the sensitivity of survey estimates to changes in sampling is pivotal to sustainable management. We present a case-study of an annual multispecies survey. Simulating distributions for four species with a spatiotemporal model, we evaluated simple random, stratified random, and systematic grid sampling designs across four sampling intensities. The systematic design yielded higher-precision estimates at all sampling intensities. However, the commonly used standard error estimator thereof resulted in mean biases from 24% to 63%. We evaluated two alternative standard error estimators that successfully mitigated these biases. Our results indicate that estimates from systematic survey designs may be robust to decreases in sampling intensity, and that analyses such as integrated stock assessments which use these estimates may mitigate model misspecification by applying alternative variance estimators.
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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 | 0.126 | 0.354 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".