Properties of fly ash stabilized haul road construction materials
Bibliographic record
Abstract
Larger haul trucks are being used at surface mines in Canada thus requiring better haul roads to carry heavier loads. The availability of good quality aggregate to build haul roads is limited for prairie coal mines. However, most of these mines are located adjacent to coal-fired electrical power plants, which produce by-product fly ash as a waste. Fly ash can be used to increase strength and stiffness of soil and road bases. Unconfined compressive strength tests conducted on various mixtures of fly ash, kiln dust, mine spoil, and coal seam partings showed that the cementing characteristics of unclassified fly ash from central Alberta coals was low. However, the addition of cement kiln dust, which is high in CaO, enabled the fly ash to exhibit significant cementing action. Mixtures of fly ash, kiln dust, and mine spoil or coal seam partings had unconfined compressive strengths of about 1 MPa and elastic moduli of about 350 MPa after 14 to 28 days. This compares favourably with compacted mine spoil or coal seam partings which have estimated unconfined compressive strengths of less than 0.4 MPa and moduli of about 50 MPa. Thus fly ash stabilized mine spoil or coal seam partings were found to have potential for use in constructing haul road base and sub-base layers since maximum tire pressures on the running surface are less than 0.7 MPa.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".