Reflectance Transformation Imaging for Roman Coin Identification: Archaeology and Education
Bibliographic record
Abstract
In 2001 the Department of Classics acquired pieces from the Diniacopolous family collection, along with a large number of coins. The majority of these coins were minted in Alexandria and vary in dates from the Hellenistic to the Byzantine period with the bulk from the Roman Imperial Period date range. While some of the coins are in decent condition and their legends and reliefs can be read with the naked eye, most require the use of imaging technology in order to be identified. This presentation will discuss results of a project currently underway to image the coins using Reflectance Transformation Imaging (RTI), a cost effective technique, which has also been used by the department at Cataraqui Cemetery to recover eroded tombstone inscriptions. While some coins were extensively eroded and thus could not be classified, the technique showed impressive results allowing most coins to be identified and dated. The presentation will also outline how RTI can be used in education, as bags of coins can be cheaply acquired by educators, thus allowing students at the primary and secondary school level to actively participate in deciphering corroded coins. This project demonstrates that RTI can be applied to a wide range of artefacts and is a valuable tool in preserving cultural heritage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".