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Record W4226239264 · doi:10.33593/v38reo2p

Long-Life Pavement for Users of an International Roadway in New Mexico

2021· article· en· W4226239264 on OpenAlexaboutno aff
Samuel S Tyson, Shiraz Tayabji

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsTruckAsphaltLevellingAsphalt pavementEngineeringCivil engineeringEnvironmental scienceTransport engineeringGeographyArchaeologyCartography

Abstract

fetched live from OpenAlex

A 36-lane-mile (60 lane-km) international roadway was rehabilitated in the United States of America (USA) during 2018 by the New Mexico Department of Transportation (NMDOT) to provide uninterrupted long-life pavement performance for commercial users of the roadway. The southern border of the USA with the country of Mexico marks the starting point of New Mexico State Road 136 (NM 136), a four-lane divided roadway that carries heavily-loaded trucks associated with the United States–Mexico–Canada Agreement (USMCA), formerly the North American Free Trade Agreement (NAFTA). Truck traffic in the dual north- and south- bound lanes of this roadway is especially high on the 9-mile (15-km) section of NM 136 between the international border and an intermodal railway facility located in the USA state of New Mexico. Prior to this rehabilitation project, the structural cross-section of NM 136 consisted of 4.5 to 6.0 inches (110 to 150 mm) of asphalt on 5.0 to 6.0 inches (130 to 150 mm) of coarse-grained soils. Prior to this project on NM 136, NMDOT had very little experience with concrete pavements and none with continuously reinforced concrete pavements (CRCPs). The structural design for this rehabilitation project utilized the existing asphalt pavement as a satisfactory base for the CRCP by milling 1.5 inches (40 mm) of the existing asphalt concrete (AC) pavement and applying a 1.5-inch (40-mm) AC levelling course followed by the CRCP. This paper presents the design and construction related details of the NM 136 CRCP project.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.508
Threshold uncertainty score0.220

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)0.0000.000

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 head0.235
Teacher spread0.222 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

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