Explicit consideration of uncertainty in Great Lakes fisheries management: decision analysis of sea lamprey (<i>Petromyzon marinus</i>) control in the St. Marys River
Bibliographic record
Abstract
We used decision analysis to assist the Great Lakes Fishery Commission (GLFC) with sea lamprey (Petromyzon marinus) control in the St. Marys River, which connects Lake Superior to Lake Huron and until recently has been a major source of sea lamprey infestation of Lake Huron. The process involved decision-makers, managers, and experts in an analysis of management options, objectives, and critical uncertainties affecting the expected performance of alternative choices. We developed quantitative assessments of uncertainty about sea lamprey demographics and about the implementation effectiveness of three pest control options: trapping adult lampreys, sterile male releases, and lampricide treatment. We used a stochastic simulation model to forecast future parasitic sea lamprey abundance in Lake Huron for 10 management options. Management options that included enhanced levels of trapping and sterile male releases exhibited the best performance for all measures. Our analysis substantially influenced the GLFC strategy for the St. Marys River, providing an example of how explicit consideration of uncertainty using decision analysis can influence fishery management decisions.
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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 | 0.005 | 0.014 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".