Making invasion models useful for decision makers: incorporating uncertainty, knowledge gaps and decision-making preferences.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<title>Abstract</title> Uncertainty is inherent in model-based forecasts of ecological invasions. In this chapter, we explore how the perceptions of that uncertainty can be incorporated into the pest risk assessment process. Uncertainty changes a decision maker's perceptions of risk; therefore, the direct incorporation of uncertainty may provide a more appropriate depiction of risk. Our methodology borrows basic concepts from portfolio valuation theory that were originally developed for the allocation of financial investments under uncertainty. In our case, we treat the model-based estimates of a pest invasion at individual geographical locations as analogous to a set of individual investment asset types that constitute a 'portfolio'. We then estimate the highest levels of pest invasion risk by finding the subset of geographical locations with the 'worst' combinations of a high likelihood of invasion and/or high uncertainty in the likelihood estimate. We illustrate the technique using a case study that applies a spatial pest transmission model to assess the likelihood that Canadian municipalities will receive invasive forest insects with commercial freight transported via trucks. The approach provides a viable strategy for dealing with the typical lack of knowledge about the behaviour of new invasive species and generally high uncertainty in model-based forecasts of ecological invasions. The technique is especially useful for under taking comparative risk assessments such as identification of geographical hot spots of pest invasion risk in large landscapes, or assessments for multiple species and alternative pest management options.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it