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Record W3160376026 · doi:10.1021/acssuschemeng.1c01242

Balancing the Use of Wax-Based Warm Mix Additives for Improved Asphalt Compaction with Long-Term Pavement Performance

2021· article· en· W3160376026 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsQueen's University
FundersAlberta InnovatesNational Natural Science Foundation of China
KeywordsWaxAsphaltDifferential scanning calorimetryCompactionMaterials scienceFourier transform infrared spectroscopyComposite materialAsphalt pavementChemical engineering

Abstract

fetched live from OpenAlex

Using wax-based warm mix additives allows contractors to lower production and compaction temperatures of asphalt, thereby reducing greenhouse and other harmful gas emissions in pavement construction. However, excessive wax can adversely affect the long-term durability of the pavement. In order to quantify solid wax, the effects of selected commercial additives on spectral and thermal properties of asphalt binder were studied by variable-temperature Fourier-transform infrared spectroscopy (VT-FTIR) and differential scanning calorimetry (DSC). The VT-FTIR reduced spectral area versus temperature plots for wax-doped asphalt binder were found to have three distinct parts from which solid wax contents could be determined. The wax precipitation temperature (WPT), obtained from DSC measurements of heat flow during cooling, was found to increase with additive content. In contrast, the wax melting out temperature (WMT), determined upon heating, appears to be independent of the additive content.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
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 communication0.0000.001
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 head0.214
Teacher spread0.200 · 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