Bibliographic record
Abstract
When conducting a backfill test campaign based on the selected mining method, there are various types of tests that are usually conducted to understand the in-situ behaviour of the material when placed underground. The unconfined compressive strength (UCS) and modulus of elasticity are the two most commonly measured parameters when dealing with a cemented paste fill material. However, when dealing with complex ore bodies or extraction techniques, additional testing, including triaxial and consolidation can be required to better understand the material’s behaviour when exposed to a sustained ground load. This is of particular interest in yielding or closing ground conditions. The modulus of elasticity results, measured by conducting a UCS test, provides information on the strength of the material and the ability to resist deformation under load when the fill is exposed vertically and horizontally. However, when dealing with long term stability and support, it is important to understand the stiffness of the material and how it will resist deformation when exposed to different loading conditions when confined. This paper will present selected triaxial and consolidation test data for a range of material blends, considerations that defined the testing program, and the nature of the tests performed. Conclusions will be presented as to the significance of the selected testing methodologies on the results obtained and their applications for industry.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".