Use of the Micro-Deval Test for Assessing Fine Aggregate Durability
Bibliographic record
Abstract
The Micro-Deval test was evaluated for its suitability in assessing the durability of fine aggregate from Virginia sources. Fine aggregates from 10 sources with known performance histories were tested with the Micro-Deval and several common aggregate quality methods. The Canadian Micro-Deval testing protocol for fine aggregates was followed. The results obtained with the Micro-Deval test showed better precision than those obtained with the conventional aggregate tests. In terms of field performance rating, the Micro-Deval test was able to differentiate between good- and poor-performing aggregates at least 80% of the time and was able to identify differences in quality between similar aggregate types with varying degrees of weathering. The Micro-Deval test is recommended as a quality control tool for fine aggregate assessment to supplement the current measures of aggregate quality. After sufficient data have been gathered and compared with data regarding field performance, it may be possible to replace the existing soundness or freeze–thaw tests with the Micro-Deval test.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".