Adaptive silviculture for climate change in the Great Lakes- St. Lawrence Forest Region of Canada: Background and design of a long-term experiment
Bibliographic record
Abstract
We present the implementation of the Adaptive Silviculture for Climate Change (ASCC) initiative at the Petawawa Research Forest (PRF) in Ontario, Canada. The study addresses the urgent need for adaptive forest management strategies in response to climate change by examining silvicultural treatments aimed at mitigating its impacts on forest ecosystems. It addresses the complex interplay between climate change projections, regional climate characteristics, and forest management practices for pine dominated forests in the Great Lakes-St. Lawrence Forest region of Canada, underscoring the importance of adaptive approaches in sustaining forest ecosystems. We outline the design and objectives of five distinct treatments—control, business-as-usual, resistance, resilience, and transition—implemented over 4 replicate blocks on a 212-ha area at the PRF. We provide detailed descriptions of each treatment’s management objectives, desired future conditions, and silvicultural strategies. We conclude by summarizing planned research efforts, including seedling survival assessments, phenological monitoring, and measuring treatment impact on fuel loads. By addressing the challenges and opportunities of climate change as part of an international research network, this research will contribute to a deeper understanding of forest ecosystem responses to climate change and inform adaptive management strategies for sustainable forest management.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".