Hyperspectral imaging solutions for the non-invasive detection and automated mapping of copper trihydroxychlorides in ancient bronze
Bibliographic record
Abstract
Abstract Ancient bronze is subject to complex degradation which can lead, in cases where copper chlorides are present, to a cyclic and self-sustaining degradation process commonly referred to as “bronze disease”. If left untreated, bronze disease can eat away at a bronze object until it is entirely deteriorated. The presence of copper trihydroxychlorides is indicative that this process is underway and therefore the detection of these corrosion products is necessary in guiding conservation of ancient bronze artefacts. In this paper we present a high spatial/spectral resolution short wave infrared (SWIR) imaging solution for mapping copper trihydroxychlorides in ancient bronze, combining hyperspectral imaging with an in-house developed unsupervised machine learning algorithm for automated spectral clustering. For this work, verification was obtained through use of an in-house developed reference database of typical ancient bronze corrosion products from several archaeological sites, and from collections of the National Museum of China. This paper also explores the suitability, and limitations, of a visible to near-infrared (VNIR) hyperspectral imaging system as a more accessible solution for mapping copper trihydroxychlorides associated with bronze disease. We suggest that our hyperspectral imaging solution can provide a non-invasive, rapid, and high resolution material mapping within and across bronze objects, particularly beneficial for analysing large collections in a museum setting.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".