X-Ray Computed Tomography In Situ: An Opportunity for Museums and Restoration Laboratories
Bibliographic record
Abstract
X-ray Computed Tomography (X-ray CT) is a sophisticated non-destructive imaging technique to investigate structures and materials of complex objects, and its application can answer many conservation and restoration questions. However, for Cultural Heritage investigations, medical CT scanners are not optimized for many case-studies: These instruments are designed for the human body, are not flexible and are difficult to use in situ. To overcome these limitations and to safely investigate works of art on site—in a restoration laboratory or in a museum—the X-ray Tomography Laboratory of the University of Bologna designed several CT systems. Here we present two of these facilities and the results of important measurement campaigns performed in situ. The first instrument, light and flexible, is designed to investigate medium-size objects with a resolution of a few tens of microns and was used for the CT analysis of several Japanese theater masks belonging to the collection of the “L. Pigorini” Museum (Rome). The second is designed to analyze larger objects, up to 200 cm and was used to investigate the collection of the so-called “Statue Vestite” (devotional dressed statues) of the Diocesan Museum of Massa.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".