Competitive effects of woody and herbaceous vegetation in a young boreal mixedwood stand
Bibliographic record
Abstract
The influence of aspen ( Populus tremuloides Michx.) and herbaceous (forb and grass) vegetation on resource availability and white spruce ( Picea glauca (Moench) Voss) growth were examined as part of a long-term experiment established in 2002 near Whitecourt, Alberta, Canada. During the 2005 growing season, we examined the effects of herbicide treatments designed to control only woody (triclopyr ester) or both woody and herbaceous (glyphosate) vegetation on leaf area index (LAI) of both the woody and herbaceous components and relationships among LAI and light, soil moisture, air temperature, soil temperature, nitrogen availability, and spruce growth. Treatments reduced LAI and increased light, soil nitrogen availability, and white spruce growth. There were no apparent effects of the treatments on soil moisture in 2005. Both the woody and herb–grass layers appear to be competing for light and soil nitrogen in this young plantation. Controlling only woody vegetation resulted in an increase in herbaceous and total LAI (dominated by the grass Calamagrostis canadensis (Michx.) Beauv.). Spot treatment, involving control of vegetation within a 2 m radius of spruce seedlings while leaving 1 m of untreated ground between treated spots, may be a promising alternative to classical broadcast treatments for establishing spruce in a mixedwood stand. Spot treatments provided good growing conditions and reduced exposure of spruce seedlings to summer and winter frost injury during the first 3 years after planting.
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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) | 0.000 | 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".