Partial cutting in old-growth boreal stands: An integrated experiment
Bibliographic record
Abstract
The uncut boreal forest of eastern Québec is largely composed of stands with an irregular structure. Traditionally, even-aged silvicultural systems have been used for these forests but a strong interest has developed in alternative approaches. In 2004, an integrated experiment was established to provide a general assessment of harvesting uneven-aged boreal forest stands with a wide variety of treatments. Here, we summarize the key results of this experiment, which involved four silvicultural treatments differing in the level of tree retention: a clearcut with advance growth protection, a severe partial cut protecting small vigorous merchantable stems (75%–90% basal area removed), and two patterns of selection cutting (35% basal area removed). We evaluated treatment effects on vegetation attributes and animal species assemblages. We also assessed whether or not selection cutting approaches could become broadly used on an operational basis by examining simple forms of application and assessing their economic profitability. We found that many attributes of old-growth forests can be maintained with selection cutting, even with simple approaches that do not invest in marking trees to cut. Unlike more severe cuts, silvicultural treatments with more than 55% tree retention largely maintain the animal assemblages associated with old forests. Financial analysis showed that selection cutting is profitable over the long time frame, but clearcutting remains more profitable. This greater profitability is related to the first entry, whereas future entries will be more profitable with selection cutting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".