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Record W4385970954 · doi:10.1139/cjce-2023-0119

Icing and aufeis in cold regions II: consequences and mitigation

2023· article· en· W4385970954 on OpenAlexafffundvenueabout
Benoit Turcotte, Ashley Dubnick, R. McKillop

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsYukon University
FundersArcticNetTransport CanadaMcMaster University
KeywordsIcingGeohazardContext (archaeology)Environmental scienceFlooding (psychology)Civil engineeringEnvironmental planningEngineeringMeteorologyGeologyGeographyLandslideGeotechnical engineering

Abstract

fetched live from OpenAlex

The process of icing and the resulting layered ice masses, called aufeis, are caused by the freezing of overflow originating from groundwater or surface water. Aufeis can directly impact infrastructure and property, most commonly through winter ice formation and spring flooding within, against, and on the surface of hydraulic structures and transportation infrastructure. They also represent a safety concern for drivers. This geohazard often needs to be managed proactively and efficiently to mitigate associated risks. This paper provides an overview of the consequences of aufeis in northwestern Canada. A total of 50 existing and novel icing and aufeis mitigation approaches are described and classified. The context of applicability for each approach is identified, considering the source of water, the type of infrastructure, and its role in the formation of aufeis. Finally, future research avenues to support the development or improvement of aufeis risk reduction techniques are presented.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.736
Threshold uncertainty score0.532

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.179
Teacher spread0.164 · 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 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

Citations10
Published2023
Admission routes4
Has abstractyes

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