Towards reliable mapping of biosecurity risk: incorporating uncertainty and decision makers' risk aversion.
Bibliographic record
Abstract
Abstract Pest risk maps are an important source of decision support when devising strategies to minimize introductions of invasive organisms and mitigate their impacts. When possible management responses to an invader include costly or socially sensitive activities, decision makers tend to follow a more certain (i.e. risk-averse) course of action. We present a new mapping technique that assesses pest invasion risk from the perspective of a risk-averse decision maker. We demonstrate the approach by evaluating the likelihood that an invasive forest pest will be transported to one of the continental US states or Canadian provinces in infested firewood that may be carried by visitors to US federal campgrounds. We test the impact of the risk aversion assumption using distributions of plausible pest arrival scenarios generated with a geographically explicit model developed from data documenting camper travel across the study area. Next, we prioritize regions of high and low pest arrival risk via application of two stochastic ordering techniques that employ, respectively, first- and second-degree stochastic dominance rules, the latter of which incorporates the notion of risk aversion. We then identify regions in the study area where incorporating risk aversion changes a region's pest risk value considerably. While both methods identified similar areas of highest and lowest risk, they differed in how they demarcated moderate-risk areas. Each method provides a tractable way to incorporate decision-making preferences into final risk estimates, and thus helps to better align these estimates with particular decision-making scenarios about an organism of concern. Overall, incorporation of risk aversion helps to refine the set of locations that could be confidently targeted for costly inspections and outreach activities.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".