The Sikkim flood of October 2023: Drivers, causes, and impacts of a multihazard cascade
Bibliographic record
Abstract
On 3 October 2023, a multihazard cascade in the Sikkim Himalaya, India, was triggered by 14.7 million cubic meters of frozen lateral moraine collapsing into South Lhonak Lake. The impact generated an ~20-meter tsunami-like impact wave, which breached the moraine and drained ~50 million cubic meters of the lake's water. The ensuing glacial lake outburst flood (GLOF) eroded ~270 million cubic meters of sediment, which overwhelmed infrastructure, including hydropower installations along the Teesta River. The physical scale and human and economic impacts of this event prompt urgent reflection on the role of climate change and human activities in exacerbating such disasters. Insights into multihazard evolution are pivotal for informing policy development, enhancing early warning systems (EWS), and spurring paradigm shifts in GLOF risk management strategies in the Himalaya and other mountain environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".