Bibliographic record
Abstract
Many ancient Greek and Latin epigraphs, once clear text on limestone and marble, have weathered and worn over the course of many centuries so that the words written on them are no longer legible. X-ray fluorescence, a common non-destructive chemical analysis technique, is extended to synchrotron-based X-ray fluorescence imaging, with which we can map the relative concentrations of many elements near the surface of a stone epigraph. We investigate the application of X-ray fluorescence imaging to these epigraphs. Initial results show an association between fluorescence intensity from trace elements near the surface of an epigraph and the presence of a glyph carved in the stone. Further, it is demonstrated that mapping this fluorescence intensity can, in some cases, improve legibility of the text beyond the capabilities of the unaided eye. Further investigations explore in more detail the usefulness of different trace elements for imaging text, potential origins of these trace elements, fluorescence intensity effects associated with the physical topography of the stone, and the application of statistical analysis techniques to X-ray fluorescence imaging data. We also apply our methods to an epigraph of uncertain provenance, demonstrating that the evidence provided by X-ray fluorescence indicates that it is a modern copy of another epigraph.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".