Automated displacement measurements on historical canvases
Bibliographic record
Abstract
Abstract Background In this paper we describe a configurable system based on laser displacement sensors for the contactless acquisition of 3D and 2D shapes of near-planar objects such as the paintings. Methods The system is based on two single-point laser triangulation sensors, a planar robot and a suite of software for driving the sensors, acquiring and post-processing the collected data. As a demonstration of the developed system we monitored three artifacts with the different aims to monitor the elastic properties of the artworks and the effectiveness of support frames in compensating the micro-climate fluctuations: the “Annunciazione” Antonello da Messina, the “Paliotto di San Domenico”, and the “Portiera Oddi-Montesperelli”. Results In the “Annunciazione” case, the canvas response to tensioning trials was analyzed. The collected data permitted to quantify a maximum displacement of 0.9 and 1.5 mm for the tensioning tests at 1 and 2 mm, respectively. In the “Portiera” case, the displacement difference between the left and right canvas sides was (1.0 ± 0.13)%, due to the inherent anisotropy of the material and by the structure of the artifact. In the “Paliotto” case, instead, minor displacement variations of the gilt leather due to the environment were observed, due to the analysis conducted prior of the restoration. Conclusions The overall obtained results demonstrated that the system is able to provide useful data for the art conservation field, with a max inaccuracy less than 100 μm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".