MétaCan
Menu
Back to cohort
Record W1968685853 · doi:10.1139/l06-143

Field performance comparison of asphalt crack-filling materials: hot pour versus cold pour

2007· article· en· W1968685853 on OpenAlexvenueno aff
Yetkin Yildirim

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsSealantAsphaltMaterials scienceComposite materialAsphalt pavementNatural rubberCrumb rubberForensic engineeringEngineering

Abstract

fetched live from OpenAlex

One of the most important components of pavement maintenance has been the sealing of cracks. The most commonly used materials for crack sealing are hot-pour sealants, such as hot rubber asphalt. However, this material can be hazardous at high operating temperatures and is more likely to be picked up by vehicle tires if the sealant is not sufficiently adhered to the pavement. Thus, cold-pour sealants have been considered as an alternative for crack sealing purposes. This study aims to compare the performance of hot-pour rubber asphalt crack sealant and cold-pour asphalt emulsion crack sealant in five different Texas districts that experience different weather conditions. The comparison includes seven different crack sealants: three cold pour and four hot pour. Five different roads in five districts were selected for comparison of the sealants. The field study results indicate that hot-pour sealants performed better than cold-pour sealants.Key words: sealants, emulsified crack sealants, crack sealing, asphalt pavements.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
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 head0.026
GPT teacher head0.253
Teacher spread0.228 · 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

Citations29
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207