To pool or not to pool data? Applying a generalized depletion model to assess American eel elver <i>Anguilla rostrata</i> fisheries from multiple rivers in Nova Scotia, Canada
Bibliographic record
Abstract
Abstract Spatial scales are important for examining health of exploited fishery stocks and guiding management actions. However, information about the optimal spatial scale is still unclear for assessment of transit fisheries, such as elver fisheries of the American eel Anguilla rostrata. We applied a generalized depletion model to assess catch and effort data from three nearby rivers (within 50 km) to test the hypothesis that modeling on pooled and separate data from nearby rivers would give similar estimates of abundance and exploitation rate. Overall, pooling data from rivers within 50 km did not result in large differences (<20% in relative difference) in estimates of abundance and exploitation rate with close mean abundance estimates and similar temporal trends in abundance, exploitation rate, and relative escapement. Pooling nearby river systems can greatly reduce modeling effort, at the cost of ignoring fine‐scale variability in elver recruitment and having coarser spatial scale for the management. When only an index of annual recruitment and exploitation rate are of interest, pooling data may be practical from different locations up to 50 km.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".