Pavement base unbound granular materials gradation optimization
Bibliographic record
Abstract
In order to characterize the effect of grain-size distribution on the behaviour of typical Quebec base UGM, the three aggregate sources were sieved into narrow granular fractions (Passing/Retained (mm/mm): 31.5/20, 20/14, 14/10, 10/5, 5/2.5, 2.5/1.25, 1.25/0.63, 0.63/0.315, 0.315/0.16, 0.16/0.08 and 0.08/0). The sieved UGM were then blended in the laboratory with the appropriate proportions to obtain samples having the gradations presented. Table 2 presents the main materials index properties determined in the characterization. The values ρdmax, ρs, wopt, Abs. and BV represents the modified proctor maximum dry density, the grain density, the optimum water content, the absorption value and the methylene blue value. The mixes volumetric characteristics at maximum dry density are the porosity nopt and the fine fraction porosity nfopt (Côté and Konrad 2003). The latter is expressed by: n V V n n n F n n n F n Ff c = = + − = = − −( )% % ( % )1 1 (1) in which nf = fine fraction porosity, %F = fine particles content, n = porosity, nc = coarse fraction porosity, VVF = voids volume in the fine fraction and VVC = voids volume in the coarse fraction. In addition, the percentage of the loose unit weight of the coarse aggregate %LUWCA determined by volume, the percentage of the rodded unit weight of the fine aggre- gate %RUWFA determined by volume, the voids within the coarse aggregates at loose unit weight %VCA and the voids within the fine aggregates at rodded unit weight %VFA are also presented. Those volumetric characteristics are determined with unit weight tests and provide useful indications about the blend and particle properties (shape, roughness, etc.).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".