Severity of summer drought as predictor for smolt recruitment in migratory brown trout (<i>Salmo trutta</i> )
Bibliographic record
Abstract
Abstract The recruitment of anadromous brown trout smolt from a small Baltic stream, frequently exposed to summer drought, was quantified for 9 years, and the effect from drought on smolt number and age composition analysed. Drought was quantified by, (a) a newly developed index quantifying the severity of summer drought (Drought Severity Index—DSI—based on amount of precipitation, monthly mean temperature and number of hours with sun), and (b) the amount of precipitation. Both DSI and precipitation were measured 1 or 2 years prior to emigration. We found highly significant (negative) relations between DSI (1 year before emigration) and both the total number of smolt, and the number of age 1 smolt. In addition, precipitation was (positively) related to total number of smolt, but DSI proved to be a stronger predictor compared to precipitation. In addition to drought, our results support a negative influence from older parr on the survival of age 0 trout. Our results indicate that recruitment of brown trout smolt from streams, regularly affected by summer drought, may be predicted approx. 6 months before the actual emigration the following spring. This offers an opportunity for managers to adapt protective measures in accordance with local and temporal changes in recruitment.
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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 | 0.000 | 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".