Prediction of the pervious surround performance of blast damage zone to reduce groundwater flow in backfilled open-pits
Bibliographic record
Abstract
Reducing groundwater flow in backfilled open-pit to limit interactions of the backfilled wastes with the environment often relies on the creation of preferential flow paths around the disposed wastes in the form of a pervious surround. The blast damage zone, which consists of an enhanced permeability zone near the pit walls, could naturally contribute to creating such preferential flow, thus eliminating the need to build a permeable envelope, reducing costs and maximising the volume for wastes deposition. The objective of this research was thus to propose parameters that could easily be accessed on the field, so practitioners could predict the flow deviation and evaluate if the BDZ is a sufficient containment structure to reduce interactions between backfilled wastes and the environment. Results showed the BDZ deviation could be predicted using the BDZ size and pit wall permeability only, and within a precision of ±15% without prior assumptions on the wastes or rock permeability. Three abacuses and one semi-analytical equation were proposed, and several criteria were derived to ensure the BDZ would act as a natural pervious surround. The results of these study should help mining operators and regulatory agencies to assess the BDZ effect on groundwater flow in backfilled open-pits.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".