Testing the reliability of the JEOL FEGSEM 6500F electron microprobe for quantitative major element analysis of glass shards from rhyolitic tephra
Bibliographic record
Abstract
Coulter, S. E., Pilcher, J. R., Hall, V. A., Plunkett, G. & Davies, S. M. 2009: Testing the reliability of the JEOL FEGSEM 6500F electron microprobe for quantitative major element analysis of glass shards from rhyolitic tephra. Boreas, 10.1111/j.1502‐3885.2009.00113.x. ISSN 0300‐9483. Electronprobe microanalysis is now widely adopted in tephra studies as a technique for determining the major element geochemistry of individual glass shards. Accurate geochemical characterization is crucial for enabling robust tephra‐based correlations; such information may also be used to link the tephra to a specific source and often to a particular eruption. In this article, we present major element analyses for rhyolitic natural glass standards analysed on three different microprobes and the new JEOL FEGSEM 6500F microprobe at Queen's University Belfast. Despite the scatter in some elements, good comparability is demonstrated among data yielded from this new system, the previous Belfast JEOL‐733 Superprobe, the JEOL‐8200 Superprobe (Copenhagen) and the existing long‐established microprobe facility in Edinburgh. Importantly, our results show that major elements analysed using different microprobes and variable operating conditions allow two high‐silica glasses to be discriminated accurately.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".