Validation and Investigation of X-Ray Luminescence and X-Ray Transmission Response for the Recovery of Diamonds Using Sensor Based Sorting
Bibliographic record
Abstract
X-ray luminescence technology is widely utilized for diamond recovery. Recently, X-ray transmission technology has become available as an alternative to X-ray luminescence technology for fine diamond recovery, offering a potential solution to recovering weakly luminescing diamonds by identifying atomic energy signatures. However, challenges exist when implementing X-ray transmission technology, including variability in the contrast between gangue particles and diamonds. Furthermore, there are operational limitations, as X-ray transmission sorters are restricted to dry conditions when processing material within the 2-4mm size range. \nThis study conducted a comprehensive analysis of 300 diamonds and 177 gangue particles to examine the effectiveness of X-ray transmission and X-ray luminescence sensor-based sorter in 2-4mm diamond recovery applications. The findings reveal that physical characteristics, such as low clarity and transparency, negatively impact diamond x-ray luminescence response. Additionally, nitrogen aggregation also affects x-ray luminescence. Specifically, diamonds that lack nitrogen, known as Type 2 diamonds, exhibit weak luminescence. These diamonds are more likely to be large and valuable, with concentrations in ore reaching up to 50% in certain lithological units. This can pose a significant economic threat to a diamond mine. \nX-ray transmission response can be reliably predicted based on diamond characteristics. Theoretically, X-ray transmission technology can identify and recover all diamonds; however, it is also susceptible to misidentifying gangue particles. X-ray luminescence response is unpredictable and inconsistent, making diamond recovery with X-ray luminescence unreliable. Additionally, X-ray luminescence can theoretically identify gangue particles but can fail to identify diamond particles. An audit of both technologies using 400 tonnes of Canadian kimberlite drill core in an operational setting revealed that the X-ray luminescence sorter achieved lower recoveries and higher yields than X-ray transmission technology.
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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".