Wave of avalanche disasters in response to colonization: a century of statistics from the world's deadliest avalanche-prone islands
Bibliographic record
Abstract
The record of avalanche disasters on Sakhalin and the Kuril Islands was always incom- plete due to the historical divide of the region between Japan and Russia. In this study we combine and analyze all available relevant information from Japanese and Russian archive sources in order to reconstruct a continuous centurial record of snow avalanche catastrophes in the region from 1910 to 2010. Despite the relatively small scale of the majority of disastrous avalanches in the area, with a total vertical drop less that 200 m, the evidence documented in this paper places Sakhalin and the Kuril Islands among the most avalanche affected areas of the world. In total, 756 fatalities and more than 238 injuries occurred in 275 accidents during 100 years (two thirds of the fatalities and accidents were among Japanese). For example, this death toll is higher than that of Canada, New Zealand or Iceland. The pattern of the fatality rate was found to decrease over time due to social factors and is different from any other considered region due to the lack of any recreationist deaths. Even if the pre- sent fatality rate is lower than that of, for example, Iceland or the USA in recent years, the per capita avalanche causality rate is among the highest in the world. Finally, a shock of avalanche disasters in response to intense colonization of the islands could be shown. Although this demon- strated wave could be considered a local issue of the past, many presently developing countries may face similar impacts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".