Mapping ecological risks with a portfolio‐based technique: incorporating uncertainty and decision‐making preferences
Bibliographic record
Abstract
Abstract Aim Geographic mapping of risks is a useful analytical step in ecological risk assessments and in particular, in analyses aimed to estimate risks associated with introductions of invasive organisms. In this paper, we approach invasive species risk mapping as a portfolio allocation problem and apply techniques from decision theory to build an invasion risk map that combines risk and uncertainty in a single map product. Location Canada. Methods We divide the study area into a set of spatial domains and treat each domain as an individual ‘portfolio’ with a unique distribution of the expected impacts of invasion. The risk of invasion is then mapped by finding nested ‘efficient’ portfolio sets that identify the geographic areas exhibiting the worst combinations of the estimated risk of invasion and the uncertainty in that estimate. For C anadian municipalities, we apply the approach to quantify the risk that a given location will receive invasive forest pests with commercial freight transported via the N orth A merican road network. We compare risk allocation techniques that employ the concepts of nested mean‐variance ( M‐V ) frontiers and second‐degree stochastic dominance. Results While both methods based on M‐V and the stochastic dominance principles identified similar areas of highest risk, they differed in how they demarcated moderate‐risk areas. Furthermore, they address uncertainty in different ways, treating it as a risk premium (in the case of nested M‐V frontiers) or producing risk‐averse delineations (in the case of stochastic dominance). Main conclusions The portfolio‐based approach offers a viable strategy for dealing with the typically wide variability in risk estimates caused by a lack of knowledge about a new invader. The methodology also provides a tractable way of incorporating decision‐making preferences into the final risk estimates and thus better aligns risk assessments with particular decision‐making scenarios about the organism of concern.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".