New mineral sensing technologies for grade engineering® and coarse gangue rejection
Bibliographic record
Abstract
Geo-sensing is the ability to routinely measure or infer rock properties at a range of scales using nondestructive scanning devices. These devices can provide information and data flows to support integrated decision making across the minerals value chain. Geo-sensing has been used for many years in minerals exploration using technologies such as satellite imaging and airborne geophysics but to date the use of sensing technologies in between the mine and mill has been very limited. The aim of this research is to develop new mineral sensing technologies that can enable grade engineered ore streams through the rejection of low value gangue or deleterious elements either in the pit or during transfer to the mill. This paper focusses on the research and development being carried out by the Cooperative Research Centre for Optimising Resource Extraction (CRC ORE) and its associated members on new sensing technologies. At the start of their development these technologies were classified on the Technology Readiness Level scale at 3 (TRL3), equivalent to a proof of concept. Laboratory testing and design is aimed at progressing these sensing technologies to TRL5 which is ready for testing at an end users site. New data obtained during the laboratory testing and design phases are presented in the paper demonstrating performance specifications such as detection limits and measurement speed which can be used to infer the pod size of gangue that can be rejected at scale. Grade Engineering® and coarse gangue rejection are considered as key drivers for the mining industry to address the higher mining, processing and environmental costs associated with ore bodies with declining grades. This is an industry wide problem and the development of mineral sensors is seen as key enabling technologies for Grade Engineering® and coarse gangue rejection. The technologies presented in this paper enhance value through early decision making of ore quality and improve the overall resource value case through improved grade and throughput engineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".