Bibliographic record
Abstract
This study evaluates the dosages of Class F fly ash, lithium nitrate and their combinations to suppress the excessive expansion caused by alkali–silica reactivity (ASR). In order to serve the proposed objective, the mortar bar specimens were prepared from (1) four dosages of Class F fly ash, such as 15, 20, 25 and 30 % as a partial replacement of Portland cement, (2) up to six dosages of lithium nitrate, such as lithium-to-alkali molar ratios of 0.59, 0.74, 0.89, 1.04, 1.19 and 1.33, and (3) the combination of lithium salt (lithium-to-alkali molar ratio of 0.74) and two dosages of Class F fly ash (15 and 20 % as a partial replacement of Portland cement). Percent contribution to ASR-induced expansion due to the fly ash or lithium content, test duration and their interaction was also evaluated. The results showed that the ASR-induced expansion decreased with an increase in the admixtures in the mortar bar. However, the specimens made with the both Class F fly ash and lithium salt produced more effective mitigation approach when compared to those prepared with fly ash or lithium salt alone. The ASR-induced expansions of fly ash or lithium bearing mortar bars by the proposed models generated a good correlation with those obtained by the experimental procedures.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".