Machine Learning and Sigillography: Using Decision Trees to Date British Seal Matrices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The availability of software libraries for machine learning, in combination with the growing number of digital sigillographic information resources, enables scholars to explore how this technology can help date medieval seal matrices. This paper presents and evaluates a pioneering system based on archival seal impression data, courtesy of the Digisig Project. The system constructs a decision tree that can be employed to date seal matrices. To explore the potential practical applications of the method, we compare its outputs with the dates assigned to seal matrices by the cataloguers of the Portable Antiquities Scheme and the Schøyen Collection catalogue. This method does not replace human cataloguers, but it can quickly and cost-effectively identify sections of existing catalogues that might benefit from revision.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it