Bibliographic record
Abstract
Until recently multi-spectral imaging in the field of archaeology has been vastly under-utilized due to the great expense of using specialized films and cameras. A great deal of data remains hidden when observing artefacts such as papyri and pottery shards (ostraca) solely under visible light (400-700nm). The writings on these artefacts are often faded and illegible resulting in much of the information they store being lost. Our approach has been to use modified commercial cameras along with a Coastal Optics 60mm multi-spectra lens to enhance the contrast of the text through the use of Ultraviolet (300-390nm) and Infrared (700-1000nm) Reflectography and computer postprocessing of the RAW images. The results are stunning. A great deal of the text on these artefacts can been made legible and subsequently studied. The underlying principle comes from the fact that pigments and minerals reacting differently to the specific bandwidths of UV and IR light, thereby producing an enhanced contrast version of the once illegible artefact. This information can be later recorded and used to further the understanding of the object itself and the civilization of which it originated. In addition, this photographic technique can be further adapted to study non-textual artefacts such as paintings. These results are consistently obtained, readily reproduced and can be adapted to study all text upon papyri, ostraca and other cultural artefacts. Moreover, the system can be easily moved onsite to museums and galleries
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".