Comminution of indicator minerals in a tumbling mill: implications for mineral exploration
Bibliographic record
Abstract
Heavy mineral dispersal trains are commonly used during mineral exploration to locate ore bodies buried at depth. Heavy minerals tend to physically break down (comminute) as they are transported away from their bedrock sources, causing them to become smaller, less frequent and commonly rounder. The size, roundness and concentration (number) of heavy mineral grains are therefore commonly used as indications of proximity to source. However, few studies have investigated the rate at which different heavy minerals break down during transport. To provide quantitative insight into this, we studied the comminution of several heavy minerals used in diamond exploration, termed kimberlite indicator minerals (KIMs), in a tumbling mill. In the experiment, pyrope garnet grains lost mass the fastest, followed by chrome diopside grains, which generally lost mass much slower, and, finally, ilmenite grains, which hardly lost any mass at all. The pyrope grains lost mass the fastest because each grain broke into tens to hundreds of angular fragments, producing abundant sand--?sized particles, in addition to abundant mud (i.e. silt and clay). By contrast, the chrome diopside and ilmenite grains remained relatively intact and lost mass primarily by edge rounding, which produced a comparatively small amount of mud and little to no sand. These results suggest that in situations where grain comminution occurs during transport, the sand and gravel (>0.063 mm) and mud (<0.063 mm) fractions of kimberlite dispersal trains have the potential to continuously change in composition downflow-specifically the mud and finer sand fractions may become progressively enriched in pyrope garnet fragments relative to fragments of other indicator minerals moving away from the source.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".