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Record W2183642767 · doi:10.1139/t2012-058

Remedying embankment thaw settlement in a warm permafrost region with thermosyphons and crushed rock revetment

2012· article· en· W2183642767 on OpenAlexvenueno aff
Wei Ma, Zhi Wen, Yu Sheng, Qingbai Wu, Dayang Wang, Wenjie Feng

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostRevetmentGeotechnical engineeringLeveeSettlement (finance)GeologyPlateau (mathematics)

Abstract

fetched live from OpenAlex

Due to the special engineering geology characteristics of permafrost, construction in permafrost regions tends to result in serious permafrost-related engineering problems. Thaw settlement induced by permafrost degradation is the principal challenge for railway construction on the Qinghai-Tibetan Plateau. It threatens the stability and safety of the railway system, especially in warm and ice-rich permafrost regions. Thaw settlement in section DK1139+780 along the Qinghai-Tibetan railway is a potential risk to the safety of the railway, and a combination of closed thermosyphons and crushed rock revetment was used to remedy permafrost warming and thaw settlement of the embankment. Based on ground temperatures and embankment deformations observed at this site since 2002, the effects of the remedial measures were evaluated. The results show that the remedial measures lowered the ground temperature and raised the permafrost table. The crushed rock slope protection acted as an insulation layer and reduced heat flux into the embankment. The thermosyphons lowered the permafrost temperature and had a good cooling effect on the underlying permafrost. The results show that the remedial measures using two-phase thermosyphons and crushed rock revetment decreased the settlement of the embankment and improved the stability of the railway system.

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 categoriesInsufficient 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 score0.355
Threshold uncertainty score0.998

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)0.0030.000

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 head0.035
GPT teacher head0.228
Teacher spread0.193 · 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

Citations99
Published2012
Admission routes1
Has abstractyes

Explore more

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