Five-year light, vegetation, and regeneration dynamics of boreal mixedwoods following silvicultural treatments to establish productive aspen–spruce mixtures in northeastern Ontario
Bibliographic record
Abstract
Given the extent of boreal mixedwoods in Canada and the challenges of maintaining their conifer component following harvest, we investigated the effects of intimate mixtures versus a mosaic spatial arrangement on mixedwood establishment and growth in northeastern Ontario. The silvicultural treatments were preharvest aerial herbicide spray, postharvest ground herbicide in conifer corridors, partial cutting, conventional conifer plantation with postharvest aerial herbicide, and an untreated reference stand. Fifth-year results suggest that preharvest herbicide application followed by clearcutting controlled trembling aspen ( Populus tremuloides Michx.) regeneration density and height growth nearly as effectively as postharvest herbicide in conifer corridors or the conventional conifer plantation treatments. Partial cutting reduced aspen regeneration in both harvested and leave corridors but did not affect shrub cover. Survival of spruce regeneration did not differ among silvicultural treatments; however, more spruce seedlings progressed from small to intermediate height classes in the preharvest spray and partial cut than in the postharvest herbicide treatment plots. Based on short-term responses in light availability, vegetation cover, and regeneration as well as cost comparisons among options, the treatment objectives were generally met: the stand has the desired species density and composition of a healthy and productive mixedwood.
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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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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".