The Canadian Ecology Centre – Forestry Research Partnership: Implementing a research strategy based on an active adaptive management approach
Bibliographic record
Abstract
Between April 2000 and March 2007, the Canadian Ecology Centre – Forestry Research Partnership funded, directed, or catalyzed approximately 145 projects. Most of these focused on knowledge and data acquisition, providing a solid foundation for a series of sensitivity and gap analyses to determine whether a long-term goal of enhancing productivity on 6 forest management units in Ontario was achievable, and more importantly, sustainable. A research strategy provided the focus for knowledge and data acquisition and the partnership facilitated integrated research, development, transfer, and implementation. Here we provide an overview of this effort, which is expected to position forest managers of the 6 forests to apply an adaptive management process to increase understanding of the response of their forests to various forest management policies and practices in the future. The strategy and approach described could be useful to other jurisdictions aiming to more closely integrate forest research and operations as well as those interested in implementing adaptive management. Key words: forest ecology, forest economics, forest inventory, forest management
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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 | 0.059 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".