Effectiveness of delayed brush cutting and herbicide treatments for vegetation control in a seven-year-old jack pine plantation in northwestern Ontario, Canada
Bibliographic record
Abstract
Efficacy of three conifer release treatments, i) single application of glyphosate (Vision™) herbicide, ii) multiple application of glyphosate herbicide, and iii) motor-manual brush cutting for controlling competing plants, particularly trembling aspen (Populus tremuloides), pin cherry (Prunus pensylvanica), green alder (Alnus viridis spp. crispa), and beaked hazel (Corylus cornuta spp. cornuta), was studied in a seven-year-old jack pine (Pinus banksiana) plantation in northwestern Ontario, Canada. The single and multiple glyphosate applications were equally effective in controlling trembling aspen and pin cherry, causing over 90% stem mortality. The brushsaw treatment caused an initial decrease followed by an increase in stem density of these two species. A high degree of stem thinning by natural mortality in the untreated control plots was observed in trembling aspen (23–46%) and pin cherry (41–69%) over four years. As with trembling aspen and pin cherry, stem density of green alder and beaked hazel initially decreased and then increased following the brushsaw treatment, mainly due to resprouting. Stem mortality in green alder and beaked hazel was 45% and 97%, respectively, two years after the operational glyphosate treatment. Competition index (CI) was low (mean CI = 52, ranging from 18 to 115) in all the plots including the untreated control. There was a significant increase in basal diameter of jack pine in the brushsaw and herbicide-treated plots compared to the control three years after the treatments. Jack pine seedlings in the brushsaw and glyphosate treated plots were taller compared to that of control but differences were not significant. Lower species richness and diversity were recorded in the herbicide-treated plots compared to the brushsaw and control plots in the third growing season following treatment.
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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) | 0.000 | 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".