Bibliographic record
Abstract
Before moving to Canada, Vincent and Olga Diniacopoulos acquired a large collection of antiquities. After settling in Montreal, they continued their activity of art dealers and collectors. In 2001, Queen’s Department of Classics and the Art Conservation Program acquired a large number of ancient pieces from the collection, including 627 Greek and Roman coins from different periods, still to be treated and identified. In my research project, I am currently working on cataloguing and identifying all of the coins in the collection. The coins in the collection are the best way for students to gain firsthand knowledge with artifacts from a research perspective. The study of ancient coins is one of the traditional fields of Classical archaeology because of their imagery, frequently complex and intricate in spite of the small space available, their direct connection to figures and events of ancient history and their use for civic and dynastic propaganda. The majority of the coins identified thus far appear to have been minted in Alexandria in Egypt, a country where the Diniacopoulos family lived for a while and acquired a large part of their collection. In my poster, I aim to present the project and to discuss a few coins that have been already identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.011 |
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".