Impacts of the December 2022 Heavy Snowfall on Tree Fall and Power Outages in Sado City, Japan
Bibliographic record
Abstract
On December 18, 2022, heavy snowfall in Sado City, Niigata Prefecture, caused widespread tree fall and bamboo collapse, and subsequent power outages. The total number of households affected by the outage reached 17,510 during the event, and restoration of the power supply required approximately 11 days. We conducted field surveys to investigate the characteristics of damage to trees and bamboo, as well as the associated power outages. This survey was conducted using both visual inspections and a vehicle equipped with an artificial intelligence-based road surface assessment system that employed a smartphone camera to document the damage to trees and bamboo and condition of road surface. Additionally, meteorological data and power outage records were analyzed to clarify the detailed weather conditions during the outage events and consider their relationship with the field survey. The results revealed that stem breakage of trees predominantly occurred in mountainous areas at relatively high elevation, whereas bamboo collapse was primarily observed in lowland areas. Analyzing meteorological data and outage records indicated that persistent strong northwesterly winds and intermittent snowfall contributed to snow accretion on trees, leading to uneven snow loading and increased susceptibility to wind damage. These conditions likely triggered widespread damage to trees and bamboo and ultimately resulted in power outages. Furthermore, the presence of dense bamboo stands and dead bamboo adjacent to power lines, which are particularly vulnerable to snow and wind damage, was considered to have contributed to the extensive power outages observed in the area.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".