Discharge-dependent covariation patterns in the population dynamics of brown trout (<i>Salmo trutta</i>) within a Cantabrian river drainage
Bibliographic record
Abstract
Patterns of spatial covariation in the population dynamics of brown trout (Salmo trutta) across Rio Esva (northwestern Spain) were explored by using the residuals from stockrecruitment relationships as indices of survival rates of spawner-to-recruit (STR), spawner-to-cohort size (STC), and spawner-to-spawner (STS). Positive correlations in pairwise comparisons among survival rates together with highly significant spatiotemporal variation in STC (74.3%) and STS (51.5%) explained by variation in STR provided evidence for persistent spatial covariation across the river drainage during the whole lifetime. Split-line regressions fitted to the survival rates versus river discharge in March (when trout emerge) highlighted the importance of discharge during, or just after, trout emergence as a major determinant of recruitment whose effects are reflected in the population over the lifetime and emphasized the synchrony between environmental processes and brown trout dynamics. Synchrony in recruitment is caused by hydrological synchrony that, in turn, is determined by climatic synchrony (rainfall) operating at the regional scale. The importance of discharge for recruitment is consistent with studies on native and introduced populations, suggesting its broad effect on the dynamics of stream brown trout across geographical regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".