Chemical and mechanical site preparation: effects on<i>Pinus contorta</i>growth, physiology, and microsite quality on grassy, steep forest sites in British Columbia
Bibliographic record
Abstract
Site preparation alleviates the effects of pinegrass (Calamagrostis rubescens Buckl.) interference on conifers in British Columbia, but little is known about interference mechanisms and appropriate site preparation methods for steep slopes. In this study, lodgepole pine (Pinus contorta Dougl. ex Loud.) was planted in pinegrass controls and small (90 × 90 cm) and large (180 × 90 cm) patches where (i) only pinegrass was removed using glyphosate or (ii) both pinegrass and the forest floor were removed using an excavator. Treatments were replicated three times in east- and west-facing clearcuts and effects were followed for 9 years. Two-year pine survival was 78% in the control and >97% in large patches. All patch treatments improved pine growth, but it was greatest in large chemical patches during the initial 6 years and in both large patch treatments thereafter. Removal of the forest floor reduced foliar and soil nutrients, increased bulk density and soil water availability, decreased porosity and aggregate stability, and reduced ectomycorrhizal diversity and richness. These changes were not observed in chemical patches. All patch treatments increased soil temperatures and reduced frost relative to controls, but more so in large patches. Pinegrass can suppress early pine performance, and large chemical patches that retain the forest floor are best for relieving the multiple environmental stressors.
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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 | 0.001 | 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".