Pavement material database - a tool to facilitate implementation of the new M-E pavement design guide
Bibliographic record
Abstract
This paper describes a proposal for using generic mechanistic material properties to perform design based on level 1 for AC and level 3 for unbound materials. These properties include the dynamic modulus of typical HMA mixes prepared according to a local standard and the resilient modulus of commonly used unbound materials (granular aggregates). Preliminary results indicate that the proposed approach is more reliable and results in lower design errors compared with the process incorporated in the current version of the M-E guide. The generic properties may be used to expedite implementation of the M-E guide in Canada until adequate mechanical testing capabilities are established. The generic pavement material properties produced at NRC are now stored in a database 'Material Library', which could be further populated using results from new tests that cover a wider range of material and construction variables; mainly to reduce the margin of error. The proposed database will not only provide the input properties needed for applying the M-E design model but will also support tasks associated with the calibration of the M-E component used to predict performance to make the model more sensitive to unique local conditions and construction practices.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.023 |
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".