Selecting intensive timber management zones as part of a forest land allocation strategy
Bibliographic record
Abstract
Establishing zones to be intensively managed for timber production is proposed by some in Canada as one way to meet the numerous and diverse objectives for which society expects forests to be managed. Concentrating timber production in such zones could make possible the expansion of protected and other areas where non-timber objectives prevail. The jury is out as to the desirability of such a land allocation strategy relative to the alternative of integrated management where low intensity timber management is practised and multiple timber and non-timber objectives are sought from the same lands. In the event some agencies opt for a zoned forest land allocation, a critical issue becomes where to situate the intensive timber management zones. To help address this issue, we present a simple and flexible quantitative process for ranking candidate areas based on their suitability for intensive timber management. The framework involves: (1) defining candidate areas, (2) specifying indicators for economic, ecological, and social criteria of suitability and scoring each candidate relative to those indicators, and (3) ranking candidates using a composite index that factors in all suitability indicators. The process is sufficiently flexible to have application in any jurisdiction, but we demonstrate its use for the 3 million ha of Crown forest in New Brunswick. We conduct several exploratory analyses designed to provide insight into selection of suitable intensive timber management zones. These analyses include controlling the geographic dispersion of candidates, quantifying sensitivity of candidate rankings to differential weighting of suitability criteria, and identifying those candidates that consistently score well across a variety of criteria weightings. Use of the ranking framework to conduct such analysis could prove of significant value in the process of selecting intensive timber management zones. Key words: intensive timber management, New Brunswick, zoning, land allocation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".