Ten Years of Vegetation Succession Following Ground-Applied Release Treatments in Young Black Spruce Plantations
Bibliographic record
Abstract
Abstract Responses of planted black spruce [Picea mariana (Mill.) BSP] and associated vegetation were studied for 10 years after conifer release options on two northeastern Ontario sites. Six treatments were compared to untreated check plots, including directed foliar application of glyphosate herbicide, basal bark treatment with triclopyr herbicide, glyphosate capsule injection with the EZ-Ject system, spot-treatment with hexazinone herbicide, manual cutting with brushsaw, and five growing seasons of annual vegetation removal with repeat applications of glyphosate. Ten years after treatment, black spruce survival averaged 86% and varied little among treatments (P > 0.5). Annual vegetation removal treatments resulted in nearly complete domination by spruce, with treated trees exhibiting 16–55% gains in height and 112–476% gains in stem volume growth over untreated trees. Despite rigorous vegetation control on these plots, each of the vegetation groups studied were well represented at the end of the observation period, including deciduous trees, tall shrubs, low shrubs, forbs, ferns, and grasses/sedges. Directed foliar treatment provided good control of herbaceous and woody vegetation around individual crop trees, providing an 8–46% gain in height and a 43–246% gain in stem volume growth. Both spruce and hardwoods shared dominance on these plots. Spot treatments with hexazinone provided similar short-term reductions in herbaceous vegetation, but tended to release shrub species that had a negative net effect on spruce growth. The other release treatments provided only short-term reductions in woody vegetation, which ultimately led to young stands dominated by deciduous tree species. North. J. Appl. For. 21(3):123–134.
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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 | 0.000 | 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".