Effect of gradation, compaction and water content on crushed waste rocks strength
Bibliographic record
Abstract
We reduced the size of the abstract to 170 words, but we cannot go lower without loosing meaning. We hope it will be ok like this --> Open-pit mining operations generate large volumes of waste rocks. It can be both economically and environmentally attractive to valorise waste rocks in mining infrastructures such as haul roads. However current practice usually consists of using waste rock directly, without any preparation or selection, thus frequently resulting in punctures, dust generation and low durability. The aim of this study was therefore to determine optimal geotechnical properties of crushed waste rocks for use in surface course layer. Crushed waste rocks were characterised in the laboratory. Their strength was evaluated using California Bearing Ratio (CBR) tests. Several compaction energies were used, and he effect of maximum particle size, fines content, and dry density on the strength were assessed. Results showed that these parameters had a significant impact on the performance of the material. The strength was maximum for an optimum FC of around 10%, a high density and in the presence of coarser particles. Two regression models were also proposed to predict the CBR value of crushed waste rocks based on their physical properties.
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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 | 0.000 | 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) | 0.002 | 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 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".