Integrated approach between pulsed thermography, near-infrared reflectography and sandwich holography for wooden panel paintings advanced monitoring
Bibliographic record
Abstract
The durability of an exterior finish is affected by the characteristics of the wood. Satisfactory finish life is usually more difficult to achieve on woods of higher density. All wood shrinks as it loses moisture and swells as it absorbs moisture, but some species are more stable than others. Species that shrink and swell the most cause more stress on paint films than woods that are more stable [1]. To this end, let us recall that a painting on wood can be considered as a layered structure: The wood support is coated with a number of superposed priming layers made from mixtures of gesso and glue. A frequent fault resulting from such a system is the formation of detached regions inside the layered structure caused by the shrinkage process of the wood support [2]. Obviously, wood deteriorates more rapidly in warm, humid regions with respect to cool or dry places [3]. The influence of wood conditions on surface coatings is a critical point that should be monitored and that depends on environmental parameters such as microclimate. To prevent and control the effects, keeping costs down, a non-destructive monitoring of wood support behavior under thermal stress is needed. In this work, an integrated approach based on traditional and innovative ( HI , PT and NIR ) techniques was conducted on a primed support of poplar wood with a complex-shape surface containing areas of artificial defects at several depths due to the influence of the support on the various layers. The obtained results could be arranged, if integrated into a multidisciplinary approach, in order to define and design the conservation of the wooden artifacts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".