Insights gained after five years of continuous GPR use in potash mines
Bibliographic record
Abstract
Potash is a mineral used primarily in fertilizers, that has been mined in the province of Saskatchewan, Canada for approximately sixty years. Continuous Boring Machines (borers) are used to mechanically cut the potash ore out of potash seams. Geological anomalies are periodically encountered during mining that can create instabilities above the mining rooms. These instabilities can be hazardous to personnel and equipment. Subtle anomalies can be difficult to visually identify within the mining rooms. Such scenarios are concerning because falls-of-ground can occur with little to no warning. Ground Penetrating Radar (GPR) is well suited to identifying anomalies above mining rooms before the ground conditions become hazardous. GPR has been used in the Saskatchewan potash mines for over 40 years. In 2013, GPR was integrated with Nutrien’s borers as a safety device, with installation on production borers commencing in 2015. There are now 32 borers equipped with GPR at 4 mines, which produce approximately 22 million tonnes of ore per year. The borer operators quickly accepted the GPR technology as it was an effective early warning device for hazardous conditions. This paper will discuss several successes and challenges faced including training of personnel on how to use the technology, and maintenance of the instrumentation. Furthermore, there were interpretation challenges due both to the position of GPR on the borers and that only one antenna is installed per borer. To address these shortcomings, we have developed and tested a new prototype which will be discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".