Developing a silvicultural framework and definitions for use in forest management planning and practice
Bibliographic record
Abstract
Lack of a management framework on which to base silviculture options has plagued the forest management planning process in Ontario. The Forest Management Planning Manual for Ontario’s Crown Forests directs that strategic silvicultural options be developed and identified in terms of the (i) applicable forest unit, (ii) associated assumptions, and (iii) extent to which they can be used on a forest management unit. In this paper, we describe a framework for classifying management (or silviculture) intensity and propose definitions for extensive, basic, intensive, and elite intensities of silviculture to support the framework’s use in planning and application. We outline how the Canadian Ecology Centre – Forestry Research Partnership, a research partnership between Tembec Inc., the Ontario Ministry of Natural Resources, and Natural Resources Canada, is considering applying these in the forest management planning process. The framework and definitions can be used to develop strategic silviculture options within an active adaptive management approach. This framework should help to reduce uncertainties associated with forest development, treatment costs, response to treatments, and success rates provided appropriate monitoring. The framework and definitions described were specifically developed for silviculture related to reforestation of even-aged boreal forests. Key words: intensive silviculture, adaptive management, 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 | 0.032 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".