Optical coherence tomography and non-linear microscopy for paintings – a study of the complementary capabilities and laser degradation effects
Bibliographic record
Abstract
This paper examines for the first time the potential complementary imaging capabilities of Optical coherence tomography (OCT) and non-linear microscopy (NLM) for multi-modal 3D examination of paintings following the successful application of OCT to the in situ, non-invasive examination of varnish and paint stratigraphy of historic paintings and the promising initial studies of NLM of varnish samples. OCT provides image contrast through the optical scattering and absorption properties of materials, while NLM provides molecular information through multi-photon fluorescence and higher harmonics generation (second and third harmonic generation). OCT is well-established in the in situ non-invasive imaging of the stratigraphy of varnish and paint layers. While NLM examination of transparent samples such as fresh varnish and some transparent paints showed promising results, the ultimate use of NLM on paintings is limited owing to the laser degradation effects caused by the high peak intensity of the laser source necessary for the generation of non-linear phenomena. The high intensity normally employed in NLM is found to be damaging to all non-transparent painting materials from slightly scattering degraded varnish to slightly absorbing paint at the wavelength of the laser excitation source. The results of this paper are potentially applicable to a wide range of materials given the diversity of the materials encountered in paintings (e.g. minerals, plants, insects, oil, egg, synthetic and natural varnish).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".