Approaches to Forecasting Damage by Invasive Forest Insects and Pathogens: A Cross-Assessment
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
Abstract Nonnative insects and pathogens pose major threats to forest ecosystems worldwide, greatly diminishing the ecosystem services trees provide. Given the high global diversity of arthropod and microbial species, their often unknown biological features or even identities, and their ease of accidental transport, there is an urgent need to better forecast the most likely species to cause damage. Several risk assessment approaches have been proposed or implemented to guide preventative measures. However, the underlying assumptions of each approach have rarely been explicitly identified or critically evaluated. We propose that evaluating the implicit assumptions, optimal usages, and advantages and limitations of each approach could help improve their combined utility. We consider four general categories: using prior pest status in native and previously invaded regions; evaluating statistical patterns of traits and gene sequences associated with a high impact; sentinel and other plantings to expose trees to insects and pathogens in native, nonnative, or experimental settings; and laboratory assays using detached plant parts or seedlings under controlled conditions. We evaluate how and under what conditions the assumptions of each approach are best met and propose methods for integrating multiple approaches to improve our forecasting ability and prevent losses from invasive pests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it