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Record W752194057

Climate Impacts and Adaptations on Roads in Northern Canada

2006· article· en· W752194057 on OpenAlexaboutno aff
Susan Tighe, Lynne Cowe Falls, Ralph Haas, Donaldson R MacLeod

Bibliographic record

VenueTransportation Research Board 85th Annual MeetingTransportation Research Board · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changePermafrostPrecipitationEnvironmental scienceFlooding (psychology)Cold climatePhysical geographyClimatologyGeographyHydrology (agriculture)GeologyMeteorologyOceanography
DOInot available

Abstract

fetched live from OpenAlex

Canada’s 900,000 km of roads covers a wide diversity of geography, activities, features and climate, with climate being characterized by 10 zones. Two of these zones cover the continuous and discontinuous areas of permafrost, but all have been considered in estimating the effects of climate change, in terms of increasing temperatures, on factors including freeze-thaw cycles, flooding and washouts, slope failures, thermal degradation, precipitation and icing. In turn, the expected outcomes of these factors on paved, gravel and snow and ice roads deterioration have been estimated. The outcomes range from minimal to definitively negative in terms of accelerated deterioration and increased costs. Some potential adaptive measures have been identified, and a case illustration of the effect of a climatic zone changing from wet, high freeze to wet low freeze with increased freeze thaw cycles is provided. This could well be the case for a northern roads situation of increasing temperatures, and the results of the case illustration indicate that for paved roads service life could be reduced by 2 to 5 years. Finally, suggestions for research on the effects of climate change on northern roads are provided, with the highest priorities related to increasing temperatures and increased freeze-thaw cycles.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.023
GPT teacher head0.303
Teacher spread0.280 · 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
Published2006
Admission routes1
Has abstractyes

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