Early effects of pre- and post-harvest herbicide application and partial cutting in regenerating aspen – jack pine mixtures in northeastern Ontario
Bibliographic record
Abstract
Silvicultural options to obtain mixtures of broadleaved and conifer tree species in boreal forests remain limited, especially for mixtures of broadleaves and shade-intolerant conifers. In this study, we tested a series of treatment packages that included variation in harvesting pattern, site preparation, renewal treatments, and timing and patterns of tending to establish trembling aspen (Populus tremuloides Michx.) and jack pine (Pinus banksiana Lamb.) mixedwoods in intimate mixtures or spatial mosaics in northeastern Ontario. Preharvest spray resulted in fewer but better growth of aspen suckers compared with postharvest spray, which both reduced sucker density and decreased their vigour. Partial cutting reduced aspen regeneration in both the harvested and leave corridors but did not affect other broadleaved species, in this case predominantly red maple (Acer rubrum L.). After treatment, the rapid development of understory vegetation (shrubs, herbs, and grasses) and regeneration reduced total light transmittance at 0.5 m but had no effect at 4.0 m after 5 years. Future stands will be jack pine dominated following both pre- and post-harvest sprays, despite higher density, more uniform distribution, and better growth of broadleaved regeneration with preharvest spray, but will be balanced broadleaf and conifer mixtures in the partial cut areas.
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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".