Assessing brown trout (<i>Salmo trutta</i>) spawning movements with multistate capture–recapture models: a case study in a fully controlled Belgian brook
Bibliographic record
Abstract
A multistate capture–recapture model was developed to estimate movements of brown trout ( Salmo trutta ) between a main stem and its headwater tributary and their survival and recapture probabilities in each stream. As all individuals entering or leaving the tributary were captured by trapping, the studied ecological system was fully controlled. The performance of multistate models combining two sources of data (trapping and electrofishing) available for 6 years was first evaluated. Realistic estimates were obtained to infer the average spawning behaviour of trout: (i) 58% returned to their original site after spawning, (ii) 9% returned to their natal site for reproduction, (iii) 55% of the ascending individuals performed natal homing. Because less informative systems are pervading, we eventually assessed the sensitivity of multistate models to the level of trapping data integration. A lack of such data led to an underestimation of movement probabilities, and we found that this effect could be compensated by electrofishing samplings.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".