Nonnative brook trout (<i>Salvelinus fontinalis</i>) and the demise of native brown trout (<i>Salmo trutta</i>) in northern boreal lakes: stealthy, long-term patterns?
Bibliographic record
Abstract
This study of 193 boreal lakes of northern Sweden suggests a long-term detrimental impact of introduced brook trout (Salvelinus fontinalis) on brown trout (Salmo trutta) populations. Thirteen of 65 (20%) brown trout populations in lakes exposed to brook trout went extinct, whereas the extinction rate in unexposed lakes was significantly lower (2%). We verified other studies that indicate that altitude strongly affects the distribution of the two species; brown trout populations in our higher altitude lakes were more sensitive to impact from brook trout. In 28 lakes above 285 m, 12 trout populations exposed to brook trout went extinct, while only one population became extinct in 37 lakes below 285 m. No effects of other environmental factors were detected (e.g., water chemistry, stocking of rainbow trout (Oncorhynchus mykiss), fish species community assembly, migration barriers, or lake morphometry on brown trout extinction). The time lag between the first record of brook trout introduction and subsequent extinction of brown trout was two decades on average (maximum 70 years). Even though further stocking of brook trout has been stopped, our analysis suggest that existing sympatric populations may continue to pose an extinction threat to brown trout.
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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 | 0.001 |
| 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".