Adapting forestry practices to address old-growth concerns in the Great LakesSt.Lawrence forests of Ontario
Bibliographic record
Abstract
The Great Lakes–St. Lawrence forest, particularly the eastern white (Pinus strobus L.) and red pine (P. resinosa Ait.) forests around Temagami and Sault Ste. Marie, was the centre of controversy regarding the treatment of old-growth forests in Ontario in the 1980s and 1990s. The controversy stemmed from changes in forest composition and structure occurring in the forest. For example, the Ontario public was concerned with the obvious reduction in numbers of large eastern white and red pine trees and their replacement with small, often poor quality, white birch (Betula papyrifera Marsh.), aspen (Populus tremuloides Michx.), and jack pine (Pinus banksiana Lamb.) trees. Over the past two decades, changes in legislation, forest planning and stand prescriptions have been made in Ontario and practices at the forest and stand levels have also changed significantly. The new practices are based on linking forest activities to a better understanding of ecological processes in the forest; in particular, linking harvest and regeneration activities to our current understanding of natural disturbances and plant adaptations. These practices attempt to match more closely the conditions found in forests with a heritage of natural disturbance and reproduction, i.e., old-growth forests. For example, stand-initiating disturbances result in some trees being killed, some being damaged, and some surviving. The new harvest practices consider tree adaptations and site factors when deciding whether a tree will be cut or not. This paper provides examples of how this ecosystem-based forestry is being applied in Ontario. Key words: old growth in Ontario, conservation of old growth, application of old growth conservation practices, stand practices for old growth, landscape practices for old growth
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".