Hierarchical model detects decadal changes in calibration relationships of single-pass electrofishing indices of abundance of Atlantic salmon in two large Canadian catchments
Bibliographic record
Abstract
Electrofishing is a commonly used technique to assess freshwater fish population abundance, and in many programs, there has been a shift in the sampling methodologies towards less laborious techniques. These new techniques usually only provide an index of abundance and require calibration with other sampling methods such as successive removal to be used for absolute abundance estimation. Using data for juvenile Atlantic salmon (Salmo salar) collected in 400 sites sampled over 21 years in two large Canadian river catchments with a single sampling protocol, we developed a hierarchical Bayesian model to account for effort, day of sampling, area of the site, and catchment effects on the relationship between the single-pass index of abundance and the fish densities, thereby illustrating the importance of carrying out a calibration exercise on a regular basis. Our work indicates that calibration relationships can change over time even with standardized sampling protocols and that these directional changes in important components of the sampling procedure can bias the estimate of population abundance and misinform the understanding of population dynamics.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".