Reconnaissance of the Effects of the MW5.7 (ML6.4) Jajarkot Nepal Earthquake of 3 November 2023, Post-Earthquake Responses, and Associated Lessons to Be Learned
Bibliographic record
Abstract
On 3 November 2023, a moment magnitude (MW) 5.7 (Local Magnitude, ML6.4) earthquake struck the western region of Nepal, one of the most powerful seismic events since 1505 in the region. Even though the earthquake was of moderate magnitude, it caused significant damage to several masonry buildings and caused slope failures in some regions. The field reconnaissance carried out on 6–9 November by the study team, following the earthquake, conducted the first-hand preliminary damage assessment in the three most affected districts—Jajarkot; West Rukum; and Salyan. This study covers the observed typical structural failures and geotechnical case studies from the field study. To have a robust background understanding, this paper examines the seismotectonic setting and regional seismic activity in the region. The observations of earthquake damage suggest that most of the affected buildings were made of stone or brick masonry without seismic consideration, while most of the reinforced concrete (RC) buildings remained intact. Case histories of damaged buildings, the patterns, and the failure mechanisms are discussed briefly in this paper. Significant damage to Khalanga Durbar, a historical monument in Jajarkot, was also observed. Medium- to large-scale landslides and rockfalls were recorded along the highway. The motorable bridge in the Bheri River suffered from broken bolts, rotational movement at the expansion joint, and damage to the stoppers. The damage observations suggest that, despite the existence of building codes, their non-implementation could have contributed to the heavy impact in the region. This study highlights that the local population faces a potential threat of subsequent disasters arising from earthquakes and earthquake-induced landslides. This underscores the necessity for proactive measures in preparedness for future disasters.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".