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Record W4410110840 · doi:10.70803/001c.137720

Behavior of Masonry Mortar Containing a Non-Harmful Antifreeze Admixture

2019· article· en· W4410110840 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

VenueThe Masonry Society Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAntifreezeMasonryMortarMaterials scienceAntifreeze proteinGeotechnical engineeringComposite materialChemistryGeologyStructural engineeringEngineering

Abstract

fetched live from OpenAlex

One of the major hurdles to a wider adoption of antifreeze admixtures in cold weather concreting applications is a lack of performance data for the increasing number of products currently available on the market. This paper assesses the performance of an existing non-harmful commercial antifreeze admixture (MNC-C15) in masonry mortar. The performance was evaluated in terms of strength gain at 7 days, 28 days and 56 days at two different temperatures (-10°C and -15°C). The potential need for heat protection before exposure to subfreezing temperatures was also evaluated. The results showed that the control mortar gained little to no strength during the curing period in subfreezing conditions. The mortar with the antifreeze admixture, on the other hand, showed appreciable strength gain even without an initial period of protection from freezing, suggesting that the admixture allowed the hydration reactions to proceed at temperatures of -10°C and -15°C. However, a freezing prevention period between 6 and 12 hours was necessary for the mortar to reach an acceptable compressive strength at those temperatures.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.530
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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