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Record W65292617 · doi:10.1520/stp15849s

Facilitating Cold Climate Pavement Drainage Using Geosynthetics

2000· book-chapter· en· W65292617 on OpenAlexaffabout
G P Raymond, RJ Bathurst

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsGeosyntheticsDrainageCold climateEnvironmental scienceGeotechnical engineeringHydrology (agriculture)GeologyClimatologyEcologyBiology

Abstract

fetched live from OpenAlex

Good highway drainage has been recognized for many centuries. The theoretical concepts are simple and the technology applicable to highways built today (1999) is widely available in the technical literature. It is widely understood that efficient drainage is essential to good highway performance independent of aggregate compacted density or aggregate stability. While the theoretical concepts are simple they are often not effective in cold climates. Indeed, for cold climates, these simple concepts are shown by field excavations described herein to be lacking in a number of aspects. Based on field excavations and performance of some selected Ontario highway locations, involving both clay and sand subgrades, recommendations are presented for the design detailing, selection and installation of geosynthetic edge drains. Installation at the investigated sites was by various techniques that included: ploughed-in-place, trench excavation, and mechanical trencher and boot. All excavated edge drains were installed as retrofits either at the time of the original pavement construction or several years later. The retrofits used the existing excavated/displaced shoulder granular material as backfill. Frost action, despite what was considered good drainage practice at the time of installation, is shown to have had a major effect on field performance.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.625
Threshold uncertainty score1.000

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)0.0010.000

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 head0.014
GPT teacher head0.197
Teacher spread0.182 · 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 designSimulation or modeling
Domainnot available
GenreOther

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

Citations4
Published2000
Admission routes2
Has abstractyes

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