A macroscale evaluation of forest management in the boreal forest of Canada : linking data and models
Bibliographic record
Abstract
Climate change is altering the nature and condition of vast areas in the boreal forest of Canada. Vulnerabilities associated with drought, fire and forest health are being observed across thousands of kilometres or macroscales. There are great uncertainties in the ecological responses across this macroscale, along with uncertainties in policy and economic responses that need to translate effectively between local and macroscale decision makers. Addressing these uncertainties requires coherent economic and policy analyses that are consistent over different spatio-temporal scales. To meet the challenges, multi-source data must be linked to provide forest information at higher spatial scales, and systems linking ecological and economic information are also needed. Given this informational need, my research question was: How can we improve the linkages of multi-source ecological and economic data to evaluate forest management decisions at macroscales? First, to improve the ecological information, I evaluated alternative multivariate methods to spatially link multi-source data and models of forest attributes for macroscale analysis. The goal was to obtain logical consistency across variables of interest, while improving accuracy and computational simplicity to the analysis. Second, to improve the economic information fed into the analysis, I used price data to develop a multivariate method for generating price information at a finer temporal scale which remains consistent with longer-term price scenarios from global land use models. Third, and finally, I developed a macroscale decision support system (quantify, query and queue, Q3) that demonstrates how to link spatial and temporal ecological and economic information to a forest land-base which is subject to climate change vulnerabilities, the western boreal forest of Canada. As an illustration of the usefulness and relevance of the Q3, I assessed the ability of mitigating drought impacts resulting from possible future climates via planting improved seedling stocks developed in tree genetics and improvement programs. Overall, the methods and the newly developed macroscale decision support system I developed link ecological (e.g., climate change impacts on forests) and economic (e.g., price change) uncertainties enabling the development of appropriate forest and environmental policies, along with forest management practices needed to implement these policies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".