Trees and Ice Storms: The Development of Ice Storm–Resistant Urban Tree Populations
Bibliographic record
Abstract
Severe ice storms occur every year in the United States and Canada, particularly in the Midwestern and eastern regions of the United States. Along with fires and wind, ice storms are a frequent and major natural disturbance factor in eastern deciduous forests. Likewise ice storms are responsible for deaths and injuries of people and cause dramatic damage and tree loss to urban forests. Ice storms annually result in millions of dollars in loss, and potentially billions of dollars in losses for extreme and widespread ice storms. Damage to electric distribution systems, blocked roadways, and property damage from fallen trees and limbs pose safety concerns and disrupt normal community functions. Tree species vary in their resistance to ice accumulation. Certain characteristics, such as weak branch junctures indicated by included bark, dead and decaying branches, a broad crown, and fine branching, increase a tree’s susceptibility to ice storm damage. Planting a diverse urban forest that includes trees resistant to ice storms and performing regular tree maintenance to avoid or remove structural weaknesses will reduce damage caused by severe ice storms. Management plans for urban trees should incorporate information on the ice storm susceptibility of trees in order to: limit potential ice damage; to reduce hazards resulting from ice damage; and to restore urban tree populations following ice storms. Susceptibility ratings of species commonly planted in urban areas are presented in this publication for use in developing and maintaining healthy urban tree populations.
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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 | 0.001 |
| 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 | 0.000 | 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".