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Record W4365814600 · doi:10.2478/ttj-2023-0016

Railway Transport Adaptation Strategies to Climate Change at High Latitudes: A Review of Experience from Canada, Sweden and China

2023· review· en· W4365814600 on OpenAlexaboutno aff
Evgeniia A. Kostianaia, Andrey G. Kostianoy

Bibliographic record

VenueTransport and Telecommunication Journal · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsnot available
FundersRussian Science Foundation
KeywordsEarthworksClimate changeChinaEngineeringCivil engineeringTransport engineeringFlooding (psychology)Climate change adaptationGeography

Abstract

fetched live from OpenAlex

Abstract Impact of climate change on railway transport manifests in a variety of consequences, such as rail buckling, rail flooding, expansion of swing bridges, overheating of electrical equipment and its damage, bridge scour, failure of earthworks, ground settlement, pavement deterioration, damage to sea walls, coastal erosion of tracks and earthworks, and an increased number of railway accidents in general. Such impacts can cause considerable disruption of railway operations and lead to substantial financial expenses for repair of the railway infrastructure. Therefore, it is crucial to include adaptation strategies already in the design phase of the railway construction to ensure stability and integrity of the railway operations. This paper provides a literature review of adaptation considerations in Canada, China and Sweden and discusses climate change challenges that these countries face in their railway systems. In conclusion, the authors provide recommendations for adaptation approaches based on the reviewed international experience which can be useful for policymakers and managers of railway companies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.632
Threshold uncertainty score0.731

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.011
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.061
GPT teacher head0.302
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2023
Admission routes1
Has abstractyes

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