Using Scenarios to Assess Possible Future Impacts of Invasive Species in the Laurentian Great Lakes
Bibliographic record
Abstract
Abstract The expected impacts of invasive species are key considerations in selecting policy responses to potential invasions. But predicting the impacts of invasive species is daunting, particularly in large systems threatened by multiple invasive species, such as North America's Laurentian Great Lakes. We developed and evaluated a scenario-building process that relied on an expert panel to assess possible future impacts of aquatic invasive species on recreational fishing in the Great Lakes. To maximize its usefulness to policy makers, this process was designed to be implemented relatively rapidly and considered a range of species. The expert panel developed plausible, internally consistent invasion scenarios for five aquatic invasive species, along with subjective probabilities of those scenarios. We describe these scenarios and evaluate this approach for assessing future invasive species impacts. The panel held diverse opinions about the likelihood of the scenarios, and only one scenario with impacts on sport fish species was considered likely by most of the experts. These outcomes are consistent with the literature on scenario building, which advocates for developing a range of plausible scenarios in decision-making because the uncertainty of future conditions makes the likelihood of any particular scenario low. We believe that this scenario-building approach could contribute to policy decisions about whether and how to address the possible impacts of invasive species. In this case, scenarios could allow policy makers to narrow the range of possible impacts on Great Lakes fisheries they consider and help set a research agenda for further refining invasive species predictions. Received February 9, 2016; accepted July 4, 2016 Published online October 14, 2016
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".