Patching together the future of forest modelling: Implementing a spatial model in the 2009 Romeo Malette Forest Management Plan
Bibliographic record
Abstract
We describe a pilot project on the Romeo Malette Forest in northeastern Ontario that used a spatially explicit strategic model as an element of the hierarchical modelling process used in forest management planning. We integrated 2 different and slightly overlapping modelling tools, the traditional aspatial Strategic Forest Management Model (SFMM) and the newer spatial Patchworks model, to form a comprehensive decision support system for planning. Results from the conventional modelling approach using only SFMM served as comparison. The pilot project’s goals included satisfying Ontario’s rigorous planning and public consultation requirements, addressing technical issues associated with using 2 separate models, and engaging planning team members in the decision-making process. Implementation of a new technology (spatial modelling) into a highly regulated organizational process presented the planning team with a series of challenges. For example, the higher resolution of the data used for the Patchworks model and changes in conventional concepts and procedures required that the planning team adjust and adapt its approach. One clear advantage of the combined approach was that the long-term effects of spatial and operational adjustments, such as landscape-level policies or harvest block deferrals, were evaluated comprehensively and immediately, leading to greater confidence that the forecast outcomes are sustainable. The hierarchical-spatial modelling process used in this pilot provides a significant improvement to forest management planning and results in improved confidence in the overall sustainability of the long-term management direction. The lessons learned have provided a base for future planning teams to take advantage of the many benefits of a decision support system that includes spatial modelling. Key words: forest management planning, spatial modelling, decision support, Patchworks
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".