Repeatability and reproducibility of micro-surfacing mixture design tests and effect of total aggregates surface areas on the test results
Bibliographic record
Abstract
The first part of this study evaluates the repeatability of the International Slurry Surfacing Association (ISSA) mixture design tests. Consistency of test results between two laboratories (MTQ and LCMB) was evaluated. Aggregate gradation and sample preparation method were varied, and the responses for various ISSA mix design test for micro-surfacing were examined. The repeatability of four ISSA mix design tests for micro-surfacing was computed. To do this, the micro-surfacing mixtures were prepared by four technicians in two separate laboratories in Quebec. The modified cohesion test, the wet track abrasion test, the loaded wheel test, and the resistance to compaction test were evaluated in this study. The effect of sample preparation method using aggregate splitting and sieve analysis on consistency of mixture design test results was also evaluated. It was observed that employing sieve analysis method for micro-surfacing mixture preparation yields better consistency in test responses. For the second part of this study, the role of aggregate gradation, and their total surface area on cohesion, resistance to abrasion, and resistance to permanent deformation of micro-surfacing mixtures was studied. Two different type III applications of micro-surfacing mixtures, which are used as rut-fill materials in high traffic area, were selected to determine the effects of aggregate total surface area on micro-surfacing mix design test responses. It was found that the micro-surfacing mixtures prepared using aggregate gradation with more fine aggregates have higher resistance to rutting, bleeding, abrasion and moisture susceptibility.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".