Subsurface imaging for panel paintings inspection: A comparative study of the ultraviolet, the visible, the infrared and the terahertz spectra
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
Abstract Infrared (IR) reflectography has been used for many years for the detection of underdrawings on panel paintings. Advances in the fields of IR sensors and optics have impelled the wide spread use of IR reflectography by several recognized Art Museums and specialized laboratories around the World. The transparency or opacity of a painting is the result of a complex combination of the optical properties of the painting pigments and the underdrawing material, as well as the type of illumination source and the sensor characteristics. For this reason, recent researches have been directed towards the study of multispectral approaches that could provide simultaneous and complementary information of an artwork. The present work relies on non−simultaneous multispectral inspection using a set of detectors covering from the ultraviolet to the terahertz spectra. It is observed that underdrawings contrast increases with wavelength up to 1700 nm and, then, gradually decreases. In addition, it is shown that IR thermography, i.e., temperature maps or thermograms, could be used simultaneously as an alternative technique for the detection of underdrawings besides the detection of subsurface defects.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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