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Record W2203757945 · doi:10.1126/science.aac8353

Geomorphic and geologic controls of geohazards induced by Nepal’s 2015 Gorkha earthquake

2015· article· en· W2203757945 on OpenAlexaff
Jeffrey S. Kargel, G. J. Leonard, Dan H. Shugar, Umesh K. Haritashya, Alexandre Bevington, E. J. Fielding, Koji Fujita, Marten Geertsema, Evan Miles, Jakob Steiner, Eric Anderson, S. R. Bajracharya, G. W. Bawden, David F. Breashears, Alton C. Byers, Brian D. Collins, Megh Raj Dhital, Andrea Donnellan, Teresa Evans, Marie-Laure Geai, M. T. Glasscoe, David A. Green, Deo Raj Gurung, Renée A. Heijenk, Andrea Hilborn, K. W. Hudnut, Charles Huyck, Walter W. Immerzeel, Liming Jiang, Randall W. Jibson, Andreas Kääb, Narendra Raj Khanal, Dalia Kirschbaum, Philip Kraaijenbrink, Damodar Lamsal, Mingyang Lv, Daene C. McKinney, Natasha K. Nahirnick, Zhuotong Nan, S. Ojha, Jeff Olsenholler, T. H. Painter, M. Pleasants, K. C. Pratima, Qiang Yuan, Bruce Raup, D. Regmi, David R. Rounce, Akiko Sakai, Shangguan Donghui, J. M. Shea, A. B. Shrestha, Aparna Shukla, D. Stumm, Marco van der Kooij, K. Voss, Brandon J. Weihs, David Wolfe, Wu Lizong, Mark R. Yoder, N. W. Young

Bibliographic record

VenueScience · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of VictoriaMinistry of Forests
FundersChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsGeologySeismologyEarth science

Abstract

fetched live from OpenAlex

The Gorkha earthquake (magnitude 7.8) on 25 April 2015 and later aftershocks struck South Asia, killing ~9000 people and damaging a large region. Supported by a large campaign of responsive satellite data acquisitions over the earthquake disaster zone, our team undertook a satellite image survey of the earthquakes' induced geohazards in Nepal and China and an assessment of the geomorphic, tectonic, and lithologic controls on quake-induced landslides. Timely analysis and communication aided response and recovery and informed decision-makers. We mapped 4312 coseismic and postseismic landslides. We also surveyed 491 glacier lakes for earthquake damage but found only nine landslide-impacted lakes and no visible satellite evidence of outbursts. Landslide densities correlate with slope, peak ground acceleration, surface downdrop, and specific metamorphic lithologies and large plutonic intrusions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.240
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations558
Published2015
Admission routes1
Has abstractyes

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