Trees and Ice Storms: The Development of Ice Storm–Resistant\nUrban Tree Populations (Second Edition)
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.\nIce storm frequency and severity within the eastern United States necessitates the incorporation of ice storm information into the urban forestry planning process. While we cannot stop ice storms from occurring, we can take steps to reduce the impact of this major forest disturbance on urban forests and the interface between forests, buildings, and infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".