Joint model inference to support RAD-based decision making for aspen management in a changing climate
Bibliographic record
Abstract
Quaking aspen is a keystone species in the U.S. Rocky Mountains, and maintaining or increasing aspen cover has been identified as a management priority throughout the region. Management efforts may benefit from knowledge of when and where treatments aimed at reducing conifer competition may promote aspen persistence in the context of ongoing climate change, and where post-treatment climate will limit long-term treatment effectiveness. We use a joint modeling approach to model the covarying canopy cover of aspen and conifers across the Greys River Ranger District in the Greater Yellowstone Ecosystem. We then project cover under future climate, both including and excluding a potential conifer competitor. We apply the resist-accept-direct framework in a modeling context to inform aspen treatment prioritization. Results suggest that warming winter temperatures have the potential to reduce aspen extent and cover, yet resist approaches (e.g., conifer removal) may support aspen persistence in select portions of the landscape. Model projections indicate the potential for aspen to migrate upslope as climate warming progresses, providing opportunities to implement direct approaches to facilitate this shift. Acceptance of aspen loss may be inevitable in areas projected to become climatically unsuitable, but uncertainty in long-term forecasts will require flexible and adaptive management.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".