A quantitative risk assessment model for the management of invasive yellow perch in Shuswap Lake, British Columbia
Bibliographic record
Abstract
I developed a quantitative risk assessment model in a Bayesian decision analysis framework to evaluate management options for the potential invasion of non-native yellow perch (Perca flavescens) in Shuswap Lake, British Columbia.Probability distributions of key model parameters were determined by eliciting expert opinion during a workshop and by a mail-out survey.The model produced distributions of weighted average probabilities of abundance and spatial distribution of yellow perch in the lake 10 years after introduction.I found that impacts of a yellow perch invasion on sockeye salmon would be best mitigated by undertaking a combination of actions including education, enforcement, rotenone, and physical removal.The rank order of management options was not sensitive to assumed carrying capacity or rate of spread.Based on my results, I recommend that sampling efforts continue in Adams and Shuswap Lakes to monitor whether yellow perch spread and quantify how they interact with sockeye salmon.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".