Performance of nano-modified concrete under freezing and low temperatures
Bibliographic record
Abstract
In this study four mixtures were prepared at a constant w/b of 0.32 with different combinations of general use cement, Class F fly ash and nano-silica sol, targeting applications in cold weather.All mixtures incorporated calcium nitrate-nitrite solution as an antifreeze admixture.The mixtures were mixed, cast and cured using two different regimes: a constant freezing temperature of -5ºC, and cyclic freezing-low temperatures (-5/5ºC), without heating or insulation during the curing period.The performance of mixtures was assessed by setting time, compressive strength and mercury intrusion porosimetry tests.In addition, scanning electron microscopy was performed to characterize the microstructure of concrete.The incorporation of nanosilica significantly enhanced the overall performance of concrete, even with fly ash, indicating its promising use for cold weather applications in late fall and early spring periods, without the need for conventional heating and insulation practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".