Mid-Rotation Impacts of Stand Tending with Glyphosate on Plant Diversity in the Boreal Forest of West-Central Alberta
Bibliographic record
Abstract
Stand tending using glyphosate to promote coniferous overstory trees has been a common practice in the boreal forests of Alberta. However, there are concerns about the impact of this practice on biodiversity of understory species. This study examined the impact of broadcast glyphosate application during the active reforestation phase, two to several years post-harvest, on forest plant diversity 25 to 40 years post-harvest. Herbicide treatments had the desired effect of shifting tree layer dominance from deciduous to coniferous species, driven by a 25-fold reduction in the density of trembling aspen (3927 vs. 154 stems·ha−1, untreated and treated, respectively). However, understory plant diversity was not significantly different between treated and untreated sites as examined by the Shannon–Wiener (H) and evenness (E) indices. Shared plant species (beta) across sites was high. Of the seven site-indicator species examined, three had significantly lower cover on treated sites: Wild sarsaparilla, low bush-cranberry and oak fern. Total understory plant cover was significantly greater in the treated portion (98.0%) versus untreated (71.4%); however, this difference was not significant when bryophytes were excluded in the analysis. The establishment, maintenance and monitoring of larger long-term trials is strongly recommended.
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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".