Sample Preparation Biases in Automated Quantitative Mineralogical Analysis of Mine Wastes
Bibliographic record
Abstract
Abstract Mineralogical information is becoming increasingly important for the interpretation and prediction of the long-term leaching behavior of mine waste rock and tailings, yet the collection of quantitative mineralogical data for these materials is complicated by biases introduced during sample preparation. Here, we present experiments with synthetic reference materials, soluble mineral (gypsum) and pulverized weathered waste rock samples to investigate potential artifacts that can be introduced during the preparation of granular sample specimen for quantitative mineralogical analysis. Our results show that, during epoxy-molding, particle segregation due to size is more important than that due to density, both of which can be effectively circumvented by cutting molds perpendicular to the orientation of settling. We also determine that sacrificing sample polish to avoid phase alteration need not impede phase attribution as long as surface roughness and slope are calibrated with sample-internal contrast references. Finally, bootstrapping analysis shows that variability in geometric and mineralogical particle parameters due to unresolved sample heterogeneity is small compared with other biases, even at particle numbers <25,000 at sizes >150 µm. Our results demonstrate the importance of quantifying potential sources of error during sample preparation in quantitative mineralogical studies on mine wastes.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".