The non-destructive determination of Pt in ancient Roman gold coins by XRF spectrometry
Bibliographic record
Abstract
The determination of trace concentrations of the platinum group elements in gold bullion can be significant for establishing the geographical location from which the gold was mined. Platinum is of particular importance for ancient gold as a marker of provenance. While many techniques have been used successfully to quantify Pt in a nearly pure Au matrix, wavelength dispersive X-ray Fluorescence Spectrometry (WDXRF) has not yet been evaluated for this application. This paper demonstrates that Pt can be determined in gold coins with a limit of detection of 20 μg g−1 (k = 3). Typical relative standard deviations were observed to range from 2% to 3% for Pt determined in gold coins (at 326 and 339 μg g−1, respectively). The low bias created from the imprinted coin design raising the coin up (compared to a completely flat coin piece) was largely overcome by making a 50 μm trough in the sample cup to lower the coin relative to the X-ray tube to compensate for the coin design. Determined Pt concentrations on the side of the coin with the imprinted design were within +1 to −13% of the values obtained with the opposite side where the design was removed by polishing (measured in a normal sample cup) using the same XRF method. This method was deemed fit for purpose for historians wanting to track changes in Pt concentration in ancient Roman gold coins over long time intervals.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".