The effect of competition from different weed species on the growth of <i>Betula pendula</i> seedlings
Bibliographic record
Abstract
The identification of less competitive weed species and infestation rates might allow weeding operations to be better targeted, help conserve local plant biodiversity, and facilitate reductions in the amount of herbicide used to achieve woodland regeneration. Therefore, the effect of competition from pure stands of Cirsium vulgare (Savi) Ten., Epilobium ciliatum Raf., Holcus lanatus L., Poa annua L., and Persicaria maculosa Gray on Betula pendula Roth was investigated over 2 years. All weed species reduced tree growth significantly compared with weed-free plots, but there were no significant differences among species. When Lolium perenne L., Rumex obtusifolius L., and Cirsium vulgare were established at four densities in plots containing newly planted B. pendula over a 1-year period, all weed species reduced tree growth, but Lolium perenne was the most competitive. Weed density had no significant effect, indicating that even sparse weed growth can have a major impact on tree performance. None of the treatments affected B. pendula survival. These results confirm the inhibitory effects of a range of weed species on tree growth, but not survival, and the capacity of B. pendula to recover in the second year after planting from an initial check in growth. Percent cover was a good explanatory variable for models developed to describe the effects of weeds on tree growth, and preliminary competition indices for the different species are presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".