Integrating forest restoration, adaptation, and proactive fire management: Rogue River Basin case study
Bibliographic record
Abstract
Uncharacteristic disturbances exacerbated by climate change are challenging forests and social systems of North America. To improve efficiency and effectiveness of forest management to address these challenges, we demonstrated structured decision-making in the collaborative development of a novel 20-year dry forest management strategy for southwestern Oregon, USA. We framed priorities and evaluated options with a wildfire risk assessment, then modeled stand-scale prescriptions to estimate management outputs (e.g., area treated, fuels reduced, and timber volume). We mapped landscape-scale objectives and used optimization software to prioritize treatment placement constrained by realistic access considerations and robust habitat protections. The resulting prioritization integrated proactive forest adaptation and fire management (ecological forest thinning, prescribed fire) with protection of imperiled species. To evaluate tradeoffs, we tested three 20-year scenarios, finding that the All-Lands scenario best mitigated wildfire risk; it reduced risk overall by 70%, to homes by 50%, and to core northern spotted owl habitat by 47%. This scenario treated 25% of the 1.9 million ha landscape, including 31% of federal land and 40% of the community at risk. Clear articulation of collaborative objectives and evaluation of scenarios have expanded partnerships and co-investment in actions supporting a shared vision of resilient southwestern Oregon forests applicable to other landscapes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".