Testing a New Approach to Quantify Growth Responses to Pruning Among Three Temperate Tree Species
Bibliographic record
Abstract
Abstract In settled areas, electrical line safety is maintained by pruning encroaching trees. Identifying key predictors of branch elongation growth rate following pruning would assist in developing predictive models and optimizing pruning cycles. However, measuring branches in trees near electrical lines is complex and challenging. This paper describes an innovative approach using a handheld laser rangefinder to safely and accurately estimate growth from the ground. In-tree and ground-based laser measurements were highly correlated. This was followed by testing for correlations between branch growth response over a number of years after pruning and many biotic and abiotic factors forFraxinus pennsylvanica, Acer platanoides, andAcer saccharinum, in the city of Montréal, Canada. In a sample of 59 trees,A. saccharinumhad the greatest branch growth, followed byF. pennsylvanica, and finallyA. platanoides. Branch growth increased following pruning and subsequently strongly declined, withA. platanoidesdeclining the fastest. Branch inclination angle was positively correlated with growth rate for two species, but not forA. saccharinum. Among the types of pruning used, directional pruning techniques resulted in the least branch regrowth rate. Tree diameter was weakly related to branch growth rates. These results suggest that while growth conditions for street trees may be perceived as homogenous, there is substantial variation in branch growth response. This variation may be related to pruning history, or unmeasured abiotic or biotic variables. Estimating pruning cycle duration is a complex task and further work is needed to develop a predictive model for more accurate estimation of return times.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".