Identifying and assessing critical uncertainty thresholds in a forest pest risk model.
Bibliographic record
Abstract
Abstract Pest risk maps can provide helpful decision support for invasive alien species management, but often fail to address adequately the uncertainty associated with their predicted risk values. This chapter explores how increased uncertainty in a risk model's numeric assumptions (i.e. its principal parameters) might affect the resulting risk map. We used a spatial stochastic model, integrating components for entry, establishment and spread, to estimate the risks of invasion and their variation across a two-dimensional gridded landscape for Sirex noctilio, a non-native woodwasp detected in eastern North America in 2004. Historically, S. noctilio has been a major pest of pine (Pinus spp.) plantations in the southern hemisphere. We present a sensitivity analysis of the mapped risk estimates to variation in six key model parameters: (i) the annual probabilities of new S. noctilio entries at US and Canadian ports; (ii) the S. noctilio population-carrying capacity at a given location; (iii) the maximum annual spread distance; (iv) the probability of local dispersal (i.e. at a distance of 1 km); (v) the susceptibility of the host resource; and (vi) the growth rate of the host trees. We used Monte Carlo simulation to sample values from symmetric uniform distributions defined by a series of nested variability bounds around each parameter's initial values (i.e.±5%, ..., ±50%). The results show that maximum annual spread distance, which governs long-distance dispersal, was the most sensitive of the tested parameters. At±15% uncertainty in this parameter, mapped risk values shifted notably. No other parameter had a major effect, even at wider bounds of variation. The methods presented in this chapter are generic and can be used to assess the impact of uncertainties on the stability of pest risk maps or to identify any geographic areas for which management decisions can be made confidently, regardless of uncertainty.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".