Estimating diadromous fish abundance during their migration by mark–recapture: remodeling combined with sequential Bayesian inference
Bibliographic record
Abstract
In diadromous species, entire cohorts migrate through narrow river corridors. Capture–mark–recapture (CMR) protocols with double trapping allow to quantify abundance and to monitor migration phenology: individuals are marked at a first trap, some of which being recaptured at a second facility, that also collects unmarked individuals. We propose a novel model that includes a day of recapture effect on the probability of passage of marked individuals at the recapture trap. A Bayesian hierarchical approach allows joint analysis across a series of years using the same protocol. Annual migrants numbers are estimated using mild assumptions about the distribution of daily numbers, and an original Bayesian sequential procedure by which we first estimate the variations of the daily capture probabilities and then transfer this information into the estimation of migrants numbers. We demonstrate our approach with a case study of salmon smolts migration in the Scorff River (France) over 25 years. We discuss the merits of the Bayesian sequential procedure which prevents from any undue influence of the numbers of unmarked fish caught on capture probabilities estimation.
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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 | 0.006 | 0.015 |
| 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 | 0.001 |
| 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".