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Record W2989015575 · doi:10.1080/1088937x.2019.1686082

Assessment of the cost of climate change impacts on critical infrastructure in the circumpolar Arctic

2019· article· en· W2989015575 on OpenAlexaboutno aff
Luis Suter, D. A. Streletskiy, N. I. Shiklomanov

Bibliographic record

VenuePolar Geography · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostCircumpolar starArcticClimate changeEnvironmental scienceCritical infrastructureEnvironmental resource managementSubsidenceGlobal warmingPhysical geographyClimatologyGeographyOceanographyGeologyStructural basin

Abstract

fetched live from OpenAlex

The Arctic is experiencing pronounced climatic and environmental changes. These changes pose a risk to infrastructure, impacting the accessibility and development of remote locations and adding additional pressures on local and regional budgets. This study estimates the costs of fixed infrastructure affected by climate change impacts in the Arctic region, specifically on the impacts of permafrost thaw. Geotechnical models are forced by climate data from six CMIP5 models and used to evaluate changes in permafrost geotechnical characteristics between the decades of 2050–2059 and 2006–2015 under the RCP8.5 scenario. Country-specific infrastructure costs are used to estimate the value of infrastructure affected. The results show a 27% increase in infrastructure lifecycle replacement costs across the circumpolar permafrost regions. In addition, more than 14% of total fixed infrastructure assets are at risk of damages due to changes in specific environmental stressors, such as loss of permafrost bearing capacity and thaw subsidence due to ground ice melt. Regions of Northern Canada and Western Siberia are projected to be particularly affected and may require additional annual spending in the excess of 1% of annual GRP to support existing infrastructure into the future.

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 score0.006
Threshold uncertainty score0.999

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)0.0020.000

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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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

Citations117
Published2019
Admission routes1
Has abstractyes

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