TRIA-Net: 10 years of collaborative research on turning risk into action for the mountain pine beetle epidemic
Bibliographic record
Abstract
Forest insects are showing increasing intensity of outbreaks and expanded ranges, and this has become a major challenge for forest managers. An understanding of these systems often depends upon detailed examination of complex interactions involving multiple organisms. In 2013, a team of researchers formed TRIA-Net, an NSERC support Strategic Network, with the explicit goal of exploring such interactions in the mountain pine beetle (MPB; Dendroctonus ponderosae Hopkins, 1902) – pine (Pinus sp.) – blue stain fungi (Ophiostomatales) system. Contributions of this network include novel genetic and genomic resources and insights, as well as exploration of how landscape structure affects MPB movements. This review highlights the unique contributions of TRIA-Net to our understanding of the MPB outbreak system. We also highlight how the insights we generated can be used to inform management strategies — including assessing stand susceptibility, predicting spread, and developing better monitoring approaches — and how the approach taken by the TRIA project can be used as a model for tackling other challenging forest insect outbreaks.
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.
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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".