Best practices in continuously (or not continuously) pouring paste backfill
Bibliographic record
Abstract
Cemented paste is established as a widely used backfill material. Mines using this type of backfill initially use conservative pouring strategies whereby a ‘plug’ is poured to a height exceeding the (containment) barricade and brow. The plug is allowed to cure to an extent that the remainder of the stope can be poured without inducing significant additional pressure on the barricade. In the last decade, more mines are applying engineering principles including engineered barricade designs, barricade pressure monitoring, specific backfill plug strength designs, and quality assurance/quality control (QA/QC) plant-based protocols to evaluate and verify how the efficiency of backfill placement can be safely optimised. There are several examples within the literature where mines have demonstrated how continuous backfilling can be safely adopted into their respective standard operating procedures. There is, however, an absence of published field data for cases where high or inconsistently low pressures at barricades limit the advisability of continuous pouring. This can create a bias in expectations. We present case study data from mines where a range of barricade pressures leads to, at best, an unproven justification for continuous pouring. Risk profiles are heightened if non-ideal conditions exist (i.e. non-engineered barricades, new operations lacking in sitespecific experience). Emphasis on the continual process of safely optimising backfilling efficiency is more helpful than a focus on the potential end result of continuous pouring. Indeed, we cite cases where operations have reverted to more conservative strategies when better appreciation of the risks of continuous pouring evolve with time or changing conditions. As technology allows the more widespread use of instrumentation to fulfil previous ‘use barricade pressure data to verify safe and efficient backfilling’ recommendations, it is important to step back and review best practice approaches and, indeed, the context of when it is feasible and when it is not feasible for continuously backfilling or accelerated backfilling to be adopted. Critically, we emphasise that instrumentation is only part of the solution to ensure safe backfilling. Definition of adequate plug strength, proven by QA/QC in terms of early age strength testing, adequate barricade designs and potentially personnel exclusion zones are also necessary.
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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 | 0.055 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".