Overview of Specifications for Unbound Granular Base Materials in Selected Canadian Provinces and Neighbouring States
Bibliographic record
Abstract
Specifications for unbound granular base materials vary among transportation agencies based on the availability of materials, climatic conditions, and function. Specifications aim to provide durable materials that meet design requirements and achieve the target design life. This paper compares Manitoba Infrastructure and Transportation (MIT) specifications for unbound granular base materials with the specifications developed by selected Canadian provinces and neighbouring States. This work is part of a research project to evaluate the effect of base material gradation on performance and, if required, update current MIT specifications for unbound granular base materials. The comparison showed that MIT specifications allow a higher percentage of fines (particles passing sieve No. 200). MIT's maximum particle size (19 mm) is generally smaller than the maximum particle size in other specifications (25 to 37.5 mm). The effect of the base material gradation and fines content on the stiffness and stability is being evaluated in the laboratory. Results of laboratory tests will be used to update current specifications, if required, and develop performance-based parameters for pavement design using the Mechanistic-Empirical Pavement Design Guide (MEPDG).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".