MétaCan
Menu
Back to cohort
Record W2071137448 · doi:10.1080/14680629.2008.9690162

Mechanistic Characterization of Superpave Asphalt Mixes in Costa Rica

2008· article· en· W2071137448 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRoad Materials and Pavement Design · 2008
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of New Brunswick
FundersUniversidad de Costa Rica
KeywordsGradationAsphaltDynamic modulusUltimate tensile strengthMaterials scienceCreepComposite materialModulusGeotechnical engineeringStructural engineeringEngineeringDynamic mechanical analysisComputer sciencePolymer

Abstract

fetched live from OpenAlex

This paper presents an investigation of the dynamic modulus, fatigue properties, and static creep of ten different Superpave mixes used in Costa Rica. The mechanistic properties were compared with empirical properties and also with equivalent Marshall Mix designs. The effect of the volumetric properties on the mechanistic and empirical properties was investigated. It was found that resilient modulus has a high correlation with Indirect Tensile strength yet it is not a good estimator for dynamic modulus. Effective binder content was found to correlate with the fatigue resistance. Asphalt Pavement Analyser (APA) results are highly influenced by the air voids of the specimens, whereas the benefits of using gap-graded gradations (e.g., SMA) is not reflected by the APA results. The paper shows that the tensile strength ratio (AASHTO T183 test) is highly sensitive to gradation and it fails to quantify moisture effects.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.577

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 head0.031
GPT teacher head0.223
Teacher spread0.192 · 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