IR Reflectography and Active Thermography on Artworks: The Added Value of the 1.5–3 µm Band
Bibliographic record
Abstract
Infrared Radiation (IR) artwork inspection is typically performed through active thermography and reflectography with different setups and cameras. While Infrared Radiation Reflectography (IRR) is an established technique in the museum field, exploiting mainly the IR-A (0.7–1.4 µm) band to probe for hidden layers and modifications within the paint stratigraphy system, active thermography operating in the IR-C range (3–5 μ m) is less frequently employed with the aim to visualize structural defects and features deeper inside the build-up. In this work, we assess to which extent the less investigated IR-B band (1.5–3 μ m) can combine the information obtained from both setups. The application of IR-B systems is relatively rare as there are only a limited amount of commercial systems available due to the technical complexity of the lens coating. This is mainly added as a so-called broadband option on regular Mid-wave infrared radiation (MWIR) (IR-C’/3–5 μ m) cameras to increase sensitivity for high temperature applications in industry. In particular, four objects were studied in both reflectographic and thermographic mode in the IR-B spectral range and their results benchmarked with IR-A and IR-C images. For multispectral application, a single benchmark is made with macroscopic reflection mode Fourier transform infrared (MA-rFTIR) results. IR-B proved valuable for visualisation of underdrawings, pencil marks, canvas fibres and wooden grain structures and potential pathways for additional applications such as pigment identification in multispectral mode or characterization of the support (panels, canvas) are indicated.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".