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Record W2060113933 · doi:10.1029/2008eo220011

Glacier and Permafrost Problems in High South Asia: International Workshop on Cryosphere and Hazards for Hindu Kush, Himalayas and Tibetan Plateau; Kathmandu, Nepal, 31 March to 2 April 2008

2008· article· en· W2060113933 on OpenAlexaboutno aff
John F. Shroder, Michael P. Bishop, Umesh K. Haritashya

Bibliographic record

VenueEos · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsGlacierPermafrostPlateau (mathematics)ChinaClimate changeGeographyMonsoonCryospherePhysical geographyPopulationGeologyMeteorologySea iceOceanography

Abstract

fetched live from OpenAlex

An outgrowth of prior conferences that had to be held separately for political reasons [ Bagla , 2006a, 2006b], a 3‐day workshop to discuss the impact of climate change on the glaciers and permafrost of high Asia hosted some 70 geoscientists from China, India, Nepal, Pakistan, and the United States and a few from Europe and Canada. The scientists concluded that a major need exists for better long‐term monitoring of glaciers in the region using satellite imagery coupled with direct observations in the field. Also emphasized was the lack of cooperation or lack of sharing of essential hydrological information between the commonly militarily opposed countries in the area. Even within each country, agencies do not necessarily share data with other agencies or with university scientists. For example, a ludicrous situation was noted wherein it is easier to obtain high‐resolution imagery and digital elevation models from scientists in opposing countries rather than domestically, where such data can be restricted or illegal to use. Such data hoarding potentially increases a population's risk because people who are trying to forecast hazardous droughts, floods, and landslides commonly cannot get appropriate information.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.035
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.230
Teacher spread0.204 · 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.

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

Citations1
Published2008
Admission routes1
Has abstractyes

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