Deployment of XRF Sensors Underground: An Opportunity for Grade Monitoring or Bulk Ore Sorting in Cave Mines
Bibliographic record
Abstract
Ore grades are monitored regularly in cave mines through drawpoint sampling. Automating grade monitoring through deploying X-ray fluorescence (XRF) sensors on the buckets of production loaders has been proposed as an alternative approach to address the issues around the traditional practice of drawpoint sampling. Bucket-mounted sensors can also be employed for bulk ore sorting underground. This study is aimed at evaluating the deployment of XRF sensors on production loaders as an opportunity for grade monitoring or bulk ore sorting in caving operations. The mill feed grade prediction performances of the drawpoint sampling program and mine planning software were assessed for the Cadia East panel cave mine. The results showed that the drawpoint samples underestimated the mill feed quality during a 10-month investigation period. The cave portions with bulk ore sorting potential were linked to the extraction level layout to estimate the number of drawpoints where sensors could be situated for diverting ore and waste. Samples obtained from the mine were tested to evaluate the ability of a lab-scale proxy of a bucket-mounted XRF sensor system to measure copper and gold grades. R-squared values of 0.84 and 0.68 were achieved between the predicted and measured copper and gold grades of the samples, respectively. Sensor test results are promising in revealing the potential to utilize XRF sensors underground. Future test work is encouraged to further validate the applicability of XRF sensors in an underground mining environment.
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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 | 0.001 | 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".