Comparative Evaluation of X-Ray Transmission and X-Ray Luminescence Sorting Technologies for Fine Diamond Recovery
Bibliographic record
Abstract
A study of 300 diamonds in the 2–4 mm size range revealed that X-ray transmission demonstrated a predictable relationship for detecting diamonds, with all diamonds being identified. In contrast, X-ray luminescence showed no consistent relationship between diamond characteristics and detection, and not all diamonds were identified using this method. When comparing the X-ray transmission response of diamonds to common gangue minerals found in dense media separation concentrates, X-ray transmission was found to incidentally detect small amounts of gangue particles. However, no such gangue detection occurred with X-ray luminescence, which responded only to diamonds. In pilot-scale tests, a belt-fed X-ray transmission sorter with a pressurized air ejection mechanism and a chute-fed X-ray luminescence sorter with a mechanical paddle ejection system were evaluated. The X-ray transmission sorter produced an average of 0.28 kg of concentrate per gram of diamonds separated, while the X-ray luminescence sorter generated 0.37 kg of concentrate per gram of diamonds separated. The X-ray transmission sorter achieved 99% diamond recovery, whereas the X-ray luminescence sorter achieved 91% diamond recovery. The higher concentrate mass obtained from the X-ray luminescence sorter is attributed to the ineffectiveness of the mechanical paddles, despite the superior contrast between gangue and diamonds in detection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".