Could ecological release buffer suppression efforts for non-native lake trout (<i>Salvelinus namaycush</i>) in Yellowstone Lake, Yellowstone National Park?
Bibliographic record
Abstract
Yellowstone Lake in Yellowstone National Park, USA, has the longest ongoing suppression program for non-native lake trout (Salvelinus namaycush) in the western USA. Harvest data from the suppression program, along with data from an assessment program initiated in 2011, was used to estimate lake trout abundance and mortality rates. Abundance and biomass estimates were used to estimate stock–recruitment dynamics, which were inputs to a simulation model forecasting responses to continued suppression. Abundance increased during 1998–2012 when total annual mortality exceeded 0.59 and declined thereafter. The fishing mortality rate required to reduce abundance was 67% greater than predicted by models that used prerecruit survival estimates from the lake trout’s native range. Prerecruit survival in Yellowstone Lake was estimated at four to six times greater than native range survival rates. Simulated abundance continued to decline if recent suppression efforts were maintained. High prerecruit survival in Yellowstone Lake likely illustrates ecological release for an invasive species in an ecosystem containing few predators or competitors and demonstrates the potential pitfalls of assuming equal demographic rates for native and non-native populations.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".