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Characterizing the Low-Temperature Performance of Hot-Pour Bituminous Sealants Using Glass Transition Temperature and Dynamic Stiffness Modulus

2009· article· en· W2012010756 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

VenueJournal of Materials in Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis of Composite Materials
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSealantMaterials scienceComposite materialStiffnessGlass transitionUltimate tensile strengthService lifeDynamic modulusAsphaltTensile testingYoung's modulusDynamic mechanical analysisPolymer

Abstract

fetched live from OpenAlex

Joint and crack sealants exposed to cold climates experience high tensile stresses. Sealants should have the ability to dissipate these stresses to perform their function properly. In cold climates, the state of sealing materials may change from rubbery to solid state due to low in-service temperatures. As a result, sealants become stiffer and less capable of dissipating the induced tensile stresses. This paper introduces a laboratory characterization method for joint sealants based on dynamic testing at low-temperatures. The dynamic mechanical analyzer test was conducted on seven hot-pour bituminous sealants in the temperature-sweep mode to characterize the stiffness-temperature behavior of sealants. Glass transition temperature, which is the boundary temperature between rubbery and solid states, was estimated for each sealant. Glass transition temperature and low-temperature stiffness can be used to predict the field performance of sealants, and to evaluate the compatibility of a sealant to a certain environment.

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 score0.126
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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