Physical and societal statistics for a century of snow-avalanche hazards on Sakhalin and the Kuril Islands (1910–2010)
Bibliographic record
Abstract
Abstract The analysis of historical avalanche data is important when developing accurate hazard maps. The record of snow-avalanche disasters on Sakhalin and the Kuril Islands is incomplete, due to the historical division into periods of Japanese and Russian rule. Here we combine and analyze data from Japanese and Russian sources to reconstruct a continuous record of avalanche catastrophes in the region from 1910 to 2010. Despite the relatively small scale of the majority of catastrophic avalanches, with a total vertical drop < 200 m, we document evidence that places the region among the most avalanche-affected areas in the world. In total, 756 fatalities and > 238 injuries have occurred in 275 incidents over a 100 year period (two-thirds of those killed were Japanese). This death toll is higher than that in Canada, New Zealand or Iceland, or non-recreational fatalities in France. A wave of avalanche disasters (1930s–60s) following intense colonization of Sakhalin and the Kuril Islands is evident. Although this ‘wave’ could be considered a local issue of the past, many presently developing countries may face similar situations. The fatality rate has decreased over time, due to social factors, and differs from that of any other region, in its absence of deaths through recreational activities. Although in recent years the fatality rate is lower than that of Iceland or the USA, the per capita avalanche casualty rate on Sakhalin and the Kuril Islands remains among the highest in the world.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".