Revealing inscriptions obscured by time on an early-modern lead funerary cross using terahertz multispectral imaging
Bibliographic record
Abstract
The presence of a corrosion layer on lead art and archæological objects can severely impede the interpretation of inscriptions, thus hampering our overall understanding of the object and its context. While the oxidation of lead that dominates corrosion may be chemically reversible via reduction, potentially providing some access to inscriptions otherwise obscured by time, corrosion damage is overall neither entirely reversible nor is the reduction process in all cases easy or feasible to carry out. In this study, by taking advantage of the unique penetration ability of terahertz radiation and the abundant frequency bands covered by a single-cycle terahertz pulse, we perform nondestructive terahertz multispectral imaging to look under the corrosion on a sixteenth century lead funerary cross (croix d'absolution) from Remiremont in Lorraine, France. The multispectral images obtained from various terahertz frequency bands are fed into a judiciously designed post-processing chain for image restoration and enhancement, thus allowing us for the first time to read obscured inscriptions that might have otherwise been lost. Our approach, which brings together in a new way the THz properties of the constituent materials and advanced signal- and image-processing techniques, opens up new perspectives for multi-resolution analysis at terahertz frequencies as a technique in archæometry and will ultimately provide unprecedented information for digital acquisition and documentation, character extraction, classification, and recognition in archæological studies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".