Estimating rates of fish movement from tag recoveries: conditioning by recapture
Bibliographic record
Abstract
Tag-recovery data are commonly used to estimate movement rates of fish stocks. Fishers report tagged fish found in their catch; however, not all recoveries are reported to fishery researchers and the rate of nonreporting is usually not known or is imprecisely estimated. To obviate the problem of nonreporting, an estimator of movement rates is proposed that does not use the number originally tagged but is fitted to the relative proportions recaptured in each cell in each time step subsequent to release. Rates of processes that occur in the tag-release spatial cell, such as short-term tagging mortality and survival, cancel from the predicted likelihood probabilities. Similarly, rates in the recapture cell for processes of ongoing tag loss, natural mortality, and tag nonreporting, if they can be reasonably approximated as uniform across cells, also cancel. Estimators are presented assuming one of two levels of auxiliary fishery inputs: (i) total mortality by cell or time step, or (ii) if mortality can be approximated as spatially uniform, effort totals in each cell, by time step. Yearly movement transition matrices were estimated for King George whiting (Sillaginodes punctata) in South Australia among 11 spatial cells from tag recoveries gathered over a period of three decades.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".