The importance of recruitment for the production dynamics of stream-dwelling brown trout (<i>Salmo trutta</i>)
Bibliographic record
Abstract
The objective was to highlight the role of recruitment for production dynamics of stream brown trout (Salmo trutta). The analysis of 51 cohorts (hatched in 19861999) at four sites of Rio Chaballos (northwestern Spain) showed sinusoidal growth patterns with more intense growth in spring and summer and reduced growth in winter. Survivor abundance described two-phase trajectories over the lifetime (10001400 days after emergence). A first phase of negligible or no mortality was prolonged for 500650 days. Severe mortality during the second phase differed among cohorts and among sites. Spatiotemporal variations in growth, mortality, density, spawner abundance, biomass, and production underlay variations in recruitment. Increased recruitment affected growth negatively (except at one site) and mortality positively. Cohort production decreased with increased growth at three sites but increased with increased growth at another site. Greater mortality typified the most productive cohorts. However, 90.8% and 83.6% of the variations in density and production, respectively, were explained by variations in recruitment. Thus, cohort size and production appeared to be determined by recruitment in which postrecruitment processes played a minor role. Recruitment reset the cohort's numerical and productive capacity, and increments in recruitment continued to increase cohort size and production over the recruitment magnitudes observed across sites and years.
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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) | 0.001 | 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".