Empirical Approach for Estimating Reference Material Heterogeneity and Sample Minimum Test Portion Mass for “Nuggety” Precious Metals (Au, Pd, Ir, Pt, Ru)
Bibliographic record
Abstract
Quantification of precious metal content is important for studies of ore deposits, basalt petrogenesis, and precious metal geology, mineralization, mining, and processing. However, accurate determination of metal concentrations can be compromised by microheterogeneity commonly referred to as the "nugget effect", i.e., spatially significant variations in the distribution of precious metal minerals at the scale of instrumental analytical beam footprints. There are few studies focused on the spatial distribution of such minerals and its detrimental effects on quantification of the existing suite of relevant reference materials (RM). In order to assess the nugget effect in RM, pressed powder pellets of MASS-1, MASS-3, WMS-1a, WMS-1, and KPT-1 (dominantly sulfides) as well as CHR-Pt+ and CHR-Bkg (chromite-bearing) were mapped with micro-XRF. The number of verified nuggets observed was used to recalculate an effective concentration of precious metals for the analytical aliquot, allowing for an empirical estimate of a minimum mass test portion. MASS-1, MASS-3, and WMS-1a did not contain any nuggets; therefore, a convenient small test portion could be used here (<0.1 g), while CHR-Pt+ would require 0.125 g and WMS-1 would need 23 g to be representative. For CHR-Bkg and KPT-1, the minimum test portion mass would have to be ∼80 and ∼342 g, respectively. Minimum test portions masses may have to be greater still in order to provide detectable analytical signals. Procedures for counteracting the detrimental manifestations of microheterogeneity are presented. It is imperative that both RM and pristine samples are treated in exactly the same way in the laboratory, lest powders having an unknown nugget status (in effect all field samples for analysis) can not be documented to be representing a safe minimum mass basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".