Comparative Analysis of Idaho and Micro-Deval Aggregate Degradation Test Methods
Bibliographic record
Abstract
In the United States, several states have adopted the Micro-Deval test (MDT) method for evaluating degradation characteristics of aggregates used in road construction. This paper compares MDT aggregate degradation data to those obtained by the Idaho degradation test (IDT) method. Simple linear regression analyses were performed on the aggregate degradation data with the resulting coefficient of determination, R2, values of 0.70 and 0.67 for IDT versus MDT for coarse aggregate and IDT versus MDT for fine aggregate, respectively. Additional models considered are (1) a weighted combination method that relies on the judgment of the engineer and (2) a multivariate regression analysis that does not involve weighting factors. In all the analyses considered, the multivariate regression had the largest R2 value of 0.75. Numerical examples showing correlations between MDT and IDT loss values are presented, followed by conclusions. Analyses used in this project may be of practical use to other state departments of transportation when adopting a new aggregate degradation test method and performing calibration with an existing test method.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".