Unravelling the effects of water temperature and density dependence on the spatial variation of brown trout (<i>Salmo trutta</i>) body size
Bibliographic record
Abstract
This study looks at the relative influence of water temperature and density dependence on the spatial variation in body size of 126 brown trout ( Salmo trutta ) cohorts from 12 Iberian rivers over a 12-year period. Mean cohort mass and length of age groups 0+ to 2+ varied significantly among sampling sites because of the concurrent effect of water temperature and density dependence. Density in suitable habitat had a limiting role that influenced potential maximum growth of cohorts, and water temperature differentiated these cohorts in two groups of sites with high and low potential maximum growth. Water temperature had a positive cumulative effect on body size of all age classes. However, body size of age-0 trout was nonlinearly influenced by short-term exposure to extreme water temperature. Thus, extremely high temperatures became a limiting factor and had deleterious effects on growth. There were intracohort and intercohort effects of density dependence throughout the life span, which were mainly due to the density in the available suitable habitat of trout of the same age or older. The present study supports the hypothesis that both density-dependent and density-independent processes are crucial for the understanding of population dynamics and that their relative importance varies across scales of space and time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".