Understanding Risk from Floods and Lindslides in the Himalayan Region: A Discussion to Enhance Resilience
Bibliographic record
Abstract
In June 2013, a cloud burst event dumped higher than normal torrential rains over Uttarakhand, a popular but sensitive Himalayan region in India. The rivers in the region overflowed to unprecedented levels causing severe landslides. The combined effects of these three hazards were extremely damaging. Nearly six thousand people perished in the disaster and many more got injured and went missing. Damage surpassed US$500 million. A discussion around making communities has been triggered by the event in the region that is considered a sacred destination for Hindu pilgrims. It is people’s belief that they should visit the four ancient temples in the region once in their lifetime. The challenge posed to the authorities is to find an approach that would allow them to address all aspects of similar event, including meteorological, hydrological, and geological. Currently, these different areas fall under different administrations. Â
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".