Low-cost estimates of mortality rate from single tag recoveries: addressing short-term trap-happy and trap-shy bias
Bibliographic record
Abstract
Conventional single tag-recovery data are widely available for stock assessments, notably of invertebrate fisheries, worldwide. Though not commonly used for this purpose, the times-at-large in single tag-recovery data provide (relatively) direct information about average mortality rate as a sample of survival times. Mortality rate is estimated using simple formulas given as functions of the mean time-at-large of tagged and recaptured animals. Here we extend an earlier time-at-large mortality estimator to address a potentially common source of bias: trap-happy or trap-shy behavior shortly following tag release. A maximum likelihood solution is derived, yielding an unbiased estimate of instantaneous mortality rate where the interval of usable times-at-large for observed recaptures may be truncated on both sides to any biologist-chosen experimental (recapture) time frame. In tests of the new doubly-truncated mortality estimator using simulated tag-recovery times-at-large, omitting the first 8 weeks of recaptures from the mortality estimate largely eliminated the bias introduced by simulated short-term trap-happy and trap-shy behavior. Bias in the mortality estimate declined by an order of magnitude more than the observed increase in standard error.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".