Predictors of Extinction Risk to Eradicate Introduced Trout Populations for Lake Restoration
Bibliographic record
Abstract
The restoration of mountain lakes affected by non-native fishes requires the removal of the introduced species. While extensive research has illuminated the physical habitats and complex food webs of mountain lake ecosystems, little is known about the self-sustaining fish populations, particularly in our study area, Waterton Lakes National Park (WLNP), Canada. We used generalized linear models to examine four population characteristics associated with the vulnerability of populations to depletion by gillnetting: 1) catch per unit effort (CPUE), 2) proportion of females, 3) proportion of mature individuals and 4) length of mature trout, as a proxy for age at maturity. There were significant differences between populations in CPUE and length of mature trout, but not in the proportion of females or mature individuals. We thus incorporated the former characteristics to rank 11 trout populations by their susceptibility to eradication. Two lakes in the Lineham basin were the best candidates for trout eradication due to their low population density and large size at maturity. The application of demographic characteristics to select introduced populations for eradication is a simple yet meaningful step in restoration commonly constrained by a lack of biological knowledge.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".