MétaCan
Menu
Back to cohort
Record W2618609918 · doi:10.1657/aaar0016-059

Automatic Weather Station Observations of the April 2014 Mount Everest Avalanche

2017· article· en· W2618609918 on OpenAlexaff
G. W. K. Moore, Paolo Cristofanelli, Paolo Bonasoni, G Verza, John L. Semple

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMountMeteorologyEnvironmental scienceClimatologyGeographyGeologyEngineering

Abstract

fetched live from OpenAlex

Instrumental records indicate a warming of approximately 0.8 °C has occurred in the Mount Everest region since the 1980s, which has resulted in a 100–300 m rise in the height at which the ground is permanently frozen as well as a retreat and thinning of Everest’s glaciers. For some time, there have been concerns that this warming and the resultant changes in the region’s glaciers may be increasing the risks for travellers to Mount Everest as well as the indigenous populations who support them. On 18 April 2014, an avalanche caused by the collapse of a large serac swept down Mount Everest’s Khumbu Ice Fall resulting in the deaths of 16 Sherpa. Although satellite imagery has been used to estimate the size of the serac, in situ data on the avalanche itself has not been available. Here we show that this event coincided with an approximate 15-min-long wind, thermal, and moisture anomaly, which was observed at the Nepal Climate Observatory-Pyramid situated 10 km from Mount Everest. We argue that this anomaly was associated with the avalanche and thereby provides some information on its scale and duration as well as a potential mechanism to monitor future events in this remote and data-sparse region.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.088
GPT teacher head0.317
Teacher spread0.229 · 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 teacher head, not a consensus.

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

Citations8
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueArctic Antarctic and Alpine ResearchSame topicCryospheric studies and observationsFrench-language works237,207