The contribution of genetics and genomics to understanding the ecology of the mountain pine beetle system
Bibliographic record
Abstract
Environmental change is altering forest insect dynamics worldwide. As these systems change, they pose significant ecological, social, and economic risk through, for example, the loss of valuable habitat, green space, and timber. Our understanding of such systems is often limited by the complexity of multiple interacting taxa. As a consequence, studies assessing the ecology, physiology, and genomics of each key organism in such systems are increasingly important for developing appropriate management strategies. Here we summarize the genetic and genomic contributions made by the TRIA project — a long-term study of the mountain pine beetle (Dendroctonus ponderosae Hopkins) system encompassing beetle, fungi, and pine. Contributions include genetic and genomic resources for species identification, sex determination, detection of selection, functional genetic analysis, mating system confirmation, hybrid stability tests, and integrated genetic studies of multiple taxa. These resources and subsequent findings have accelerated our understanding of the mountain pine beetle system, facilitating improved management strategies (e.g., enhancements to stand susceptibility indices and predictive models) and highlighting mechanisms for promoting resilient forests. Further, work from the TRIA project serves as a model for the increasing number and severity of invasive and native forest insect outbreaks globally (e.g., Dutch elm disease and thousand cankers disease).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".