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Permafrost-Related Geohazards and Infrastructure Construction in Mountainous Environments

2017· reference-entry· en· W2771562154 on OpenAlexaff
Lukas U. Arenson, Matthias Jakob

Bibliographic record

VenueOxford Research Encyclopedia of Natural Hazard Science · 2017
Typereference-entry
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsPermafrostClimate changeAggradationEarth scienceHydropowerNatural (archaeology)Environmental resource managementGlobal warmingNatural hazardGeologyEnvironmental planningEnvironmental scienceGeographyEngineeringGeomorphologyMeteorologyStructural basin

Abstract

fetched live from OpenAlex

Abstract Mountain environments, home to about 12% of the global population and covering nearly a quarter of the global land surface, create hazardous conditions for various infrastructures. The economic and ecologic importance of these environments for tourism, transportation, hydropower generation, or natural resource extraction requires that direct and indirect interactions between infrastructures and geohazards be evaluated. Construction of infrastructure in mountain permafrost environments can change the ground thermal regime, affect gravity-driven processes, impact the strength of ice-rich foundations, or result in permafrost aggradation via natural convection. The severity of impact, and whether permafrost will degrade or aggrade in response to the construction, is a function of numerous parameters including climate change, which needs to be considered when evaluating the changes in existing or formation of new geohazards. The main challenge relates to the uncertainties associated with the projections of medium- (decadal) and long-term (century-scale) climate change. A fundamental understanding of the various processes at play and a good knowledge of the foundation conditions is required to ascertain that infrastructure in permafrost environment functions as intended. Many of the tools required for identifying geohazards in the periglacial and appropriate risk management strategies are already available.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.706
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.305
Teacher spread0.275 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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