Bibliographic record
Abstract
The current inclination towards establishing a mechanistic pavement analysis scheme to support the rehabilitation design will require the adoption of mechanical materials properties. Mechanistic properties are needed for establishing the material-mechanics link, which represents the most effective approach for accurately predicting the response of road structures and their performance. Physical characteristics served early design practices with a common understanding among users that a more robust approach is needed to effectively address rehabilitation design requirements. This paper discusses current attempts to improve material characterization based on the outcome of mechanical tests focusing on unbound materials. The paper concluded that simplifying assumptions built in the proposed testing schemes and the manner that these properties are determined overlooked other critical behaviour indicators. Results of field and laboratory investigations highlight the need for capturing permanent deformation and the paper describes a more effective characterization technique for unbound material. Following this new approach, conventional resilient modulus and permanent deformation determinations were examined for a variety of native soils and processed material (crushed stones). Implementation of the new characterization technique in analytical models is also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".