Using Reflectance Transformation Imaging (RTI) to Record and Interpret Weathered Tombstones in the Cataraqui Cemetery
Bibliographic record
Abstract
Once our history is lost, what can be done to recover it? The information provided on tombstones is an important source for historians and genealogists but is easily lost as many tombstones are weathered or damaged over the course of decades of exposure to the elements. For many, tombstones are the only record that survives which is why it is important to collect its information. Last year a project was run in order to record degrading tombstones at the Cataraqui Cemetery in Kingston, Ontario using Reflectance Transformation Imaging which is a more cost-effective technique compared to other methods and the results are easily read by laypeople. This technique produced a clear image of the inscriptions which areno longer visible to the naked eye. However, where the damage was too extensive archival data was used to fill in the missing pieces. Thus in some cases a full record can be recovered of the tombstone’s inscription through a combination of research and imaging. This pilot project has demonstrated RTI as a potent technique to record local heritage and preserve genealogical information that would be otherwise lost to the ravages of time.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".