Bias in survival estimates from tag-recovery models where catch-and-release is common, with an example from Atlantic striped bass (<i>Morone saxatilis</i>)
Bibliographic record
Abstract
Survival rate is underestimated when tag-recovery models include tags recovered from harvested and caught-and-released fish. The magnitude of the bias depends on tag-recovery rate, proportion of catch released alive, and reporting rate; changes in these factors over time confound temporal changes in survival. The bias is of potential concern for any tagging study where catch-and-release is mandatory or practiced voluntarily. The bias is of concern particularly for the Atlantic striped bass (Morone saxatilis) tagging study where catch-and-release is common and anglers commonly remove the tag upon capture regardless of fish disposition. Biased estimates of striped bass survival did not change with changes in harvest regulation during the mid-1990s. However, bias-adjusted estimates of survival showed a decrease, which corresponds to the regulatory change made in 1995. Year-specific reporting rate is critical to bias adjustment, underscoring the need for reward tags in fish tagging studies. Tag-recovery modeling allows for a diverse set of models, each of which can produce widely different estimates with far-reaching consequences for management. We applied model averaging to base inference on a weighted average of parameter estimates and to account for model selection uncertainty.
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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 | 0.039 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".