Does formative pruning improve the form of broadleaved trees?
Bibliographic record
Abstract
Formative pruning is the pruning of young trees before canopy closure to encourage the development of a single straight stem at least 6 m in height. The use of formative pruning has been widely recommended; however, this guidance lacks a scientific basis. The experiments described here examined the effects of four levels of formative pruning on precanopy closure stands of European ash (Fraxinus excelsior L.), cherry (Prunus avium L.), European beech (Fagus sylvatica L.), and English oak (Quercus robur L.). For the faster growing ash and cherry, two prunings were applied over a 3-year period; for the slower growing oak and beech, there were four prunings over 4–6 years. Form and growth were assessed for up to 9 years after the last pruning treatment. A moderate intensity of formative pruning that removed forks and large branches showed some potential to improve the form of oak and beech. However, there were no form improvements for any level of formative pruning applied to ash or cherry. Attempting to produce the quality of timber required by management objectives by minimizing the number of trees planted and applying formative pruning is risky and likely to fail. A more secure way of obtaining quality improvement is to use traditional pruning after a period of canopy closure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".