Short- and long-term effects of sodium chloride on strength and durability of coal fly ash stabilized with carbide lime
Bibliographic record
Abstract
The research presented aims to quantify the influence of the curing period (t), dry unit weight (γ d ), lime amount (L), and the addition of sodium chloride (NaCl) on the short- and long-term behaviors of coal fly ash – carbide lime blends. Strength and wet–dry durability tests were carried out for differing values of porosity (η), L, and curing time (t) on mixtures that either contained small amounts of NaCl or contained none. Addition of NaCl to the mixtures resulted in significant increase in early strength gain when compared to specimens without NaCl, which demand longer curing periods to reach similar strength values. The addition of NaCl to coal fly ash – carbide lime blends reduced the accumulated loss of mass (ALM) after 12 wet–dry brushing cycles for specimens at early stages of curing (about 50% for 7 days). Equivalence in the unconfined compressive strength (q u ) and ALM (after 12 wet–dry cycles) between the specimens with and without NaCl is achieved in the long term. Finally, a variance analysis performed regarding q u and ALM results yielded that the order of importance of the controllable factors changed from t, NaCl addition, γ d , and L for strength to γ d , t, NaCl addition, and L for ALM (after 12 wet–dry cycles) .
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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".