Appraisal of Key Abiotic Parameters Affecting Street Tree Growth
Bibliographic record
Abstract
Street trees must cope with severe environmental conditions. Identification of factors that modulate their survival and growth is a key process for successful management. To estimate these factors, contingency analyses were carried out using abiotic data collected locally for Norway maple (Acer platanoides L.), silver maple (Acer saccharinum L.), hackberry (Celtis occidentalis L.), green ash (Fraxinus pennsylvanica Marsh.), honeylocust (Gleditsia triacanthos L.), littleleaf linden (Tilia cordata Mill.), and Siberian elm (Ulmus pumila L.) trees. Also, comparable analyses were performed on a merged all-species data set. Whereas sharp differential growth was found between commercial zones and residential areas, studied species demonstrated ecological tolerance as individuals were found normally or vigorously growing in severe conditions. In this situation, ample irradiation intensity might be an important factor contributing to urban tree growth. Statistical analyses led to the identification of a threshold level (80% of total potential irradiation) above which optimal growth was found in commercial zones. Research results also brought out the importance of the underlying surficial deposits: slow growth rates were linked to the presence of sand/gravel deposits. Finally, higher soil de-icing salt concentrations were found in tree pits where trees were transplanted closer to the curb and on wider streets.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".