Addressing catch mechanisms in gillnets improves modeling of selectivity and estimates of mortality rates: a case study using survey data on an endangered stock of Arctic char
Bibliographic record
Abstract
Estimation of fish stock size distributions from survey data requires knowledge about gear selectivity. However, selectivity models rest on assumptions that seldom are analyzed. Departures from these can lead to misinterpretations and biased management recommendations. Here, we use survey data on great Arctic char (Salvelinus umbla) to analyze how correcting for entanglement of fish and nonisometric growth might improve estimates of selectivity curves, and subsequently estimates of size distribution and age-specific mortality. Initial selectivity curves, using the entire data set, were wide and asymmetric, with poor model fits. Removing potentially nonmeshed fish had the greatest positive effect on model fit, resulting in much narrower and less asymmetric selection curves, while attempting to take nonisometric growth into account, by using girth rather than length, improved model fit but not as much. Using simulations we show that correcting for both entanglement and size selectivity produces accurate estimates of mortality rates, while correcting for size selectivity only does not. Our study demonstrates an approach that increases the accuracy of estimates of fish size distributions and mortality rates from survey data.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 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".