Hurricane Igor Impacts at Northern Latitudes: Factors Influencing Tree Fall in an Urban Setting
Bibliographic record
Abstract
Hurricane Igor was a Category 1 hurricane when it passed the island of Newfoundland, Canada, causing extensive damage. Hurricanes are uncommon at northern latitudes, and boreal species are not adapted to hurricane-force winds. Moreover, much of the storm damage was in the urban area of the City of St. John’s, where there are also numerous non-native trees. This research tested whether there were attributes of trees (e.g., height, diameter at breast height, slenderness, species, age, or distance to nearest tree) that may have influenced whether a tree fell or was left standing. The study authors sampled 70 trees and found that DBH was a significant predictor of tree fall (snapping or uprooting). Conifers were no more or less likely to fall in the storm than deciduous trees, nor were native trees more or less susceptible to wind damage than non-natives. These results suggest that for a boreal, urban ecosystem, there are no target species available that could be planted strategically to minimize risk of tree fall in a major wind event. Thus, to minimize storm damage to human-built infrastructure in regions where hurricanes are rare, the best strategy would be to avoid having large trees located in close proximity to infrastructure.
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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 | 0.001 | 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".