Spatial Geochemistry: A Characterisation of Heterogeneity in Reference Materials
Bibliographic record
Abstract
Geochemical reference materials (RMs) for microbeam techniques are typically characterised by averages and dispersion statistics (e.g., standard deviation, variance) that are calculated for a number of measurements (beam shots). It is proposed that the mapping of RMs will add spatial information that better characterises the grouping and magnitudes of the heterogeneities and provides the information necessary to define a minimum analytical mass. A simple mathematical solution is proposed, which can be easily computed and understood. The analogous notions to sill and range from geostatistics are applied to the minimum analytical mass versus the relative standard deviation. To assess grouping and magnitudes of the heterogeneities, a ‘proximity number’ is computed for each average value ± ‘n’ standard deviations (magnitude). Different chemical anomalies have been simulated to demonstrate the behaviour of the proximity number. To further test the proposed spatial geochemistry concept, sulfide‐ and oxide‐bearing RMs have been selected because many are crippled with nugget effect. They have been mapped with a micro‐XRF apparatus, and results are presented for CHR‐Bkg, CHR‐Pt+, MASS‐1, MASS‐3, WMS‐1 and WMS‐1a. MASS‐1 and MASS‐3 are the most suitable RMs for microbeam techniques. Spatial geochemistry offers a new approach to better characterise reference materials.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".