Climate sensitive growth and yield models in Canadian forestry: Challenges and opportunities
Bibliographic record
Abstract
Growth and yield models in forest management planning are used to project future forest conditions and estimate quantities such as wood volume and biomass. These models are crucial for assessing forest sustainability, however, some models currently used in Canada do not adequately account for climate and other environmental variables, which limits their effectiveness under a changing climate. Climate-sensitive growth and yield models (CSGYMs) are therefore urgently needed to support forest management decisions. The Canadian Forest Service (CFS) has developed a strategic plan to advance climate-sensitive growth and yield modeling in Canada through collaboration with provincial and territorial agencies, as well as other partners. The primary objective of this plan is to provide a national-level modelling approach to predicting and managing forest growth, mortality, and other ecosystem services. The climate sensitive growth and yield modelling initiative emphasizes collaboration, open data, and open-source code principles to ensure widespread accessibility and uptake of models, thus contributing to the sustainable management of forest resources. This technical review reports on the status of growth and yield models currently applied in each province and territory, assesses the level of climate sensitivity associated with each of these models, synthesizes the relevant modeling approaches and input data required to implement climate sensitivity into these models, and suggests possible pathways for achieving CSGYM at a national scale. Widespread collaboration will be the key to advancing the development of CSGYMs.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".