Evaluation of Salt-Induced Damage to Aged Wood of Historical Wooden Buildings
Bibliographic record
Abstract
Salt is a common cause of damage to building materials used in cultural and historical buildings. The damage to aged wood in historical wooden buildings has not been extensively studied, resulting in the need for a more detailed analysis. In this work, Yingxian Wooden Pagoda, a typical historical wooden structure, was taken as the research object. Multichemical analyses were conducted to evaluate and understand the salt-induced damage to the aged wood using a scanning electron microscope equipped with an energy-dispersive X-ray spectrometer, sulphur K-edge X-ray absorption near-edge structure spectroscopy, X-ray fluorescence spectroscopy, X-ray powder diffraction, and attenuated total reflectance fourier transformed infrared spectroscopy. The results showed the presence of invasive salt crystallisations and ions in the aged samples. The source of these invasive elements was deduced by identifying the type, amount, and valency of the elements; they were found to be derived from environmental factors such as acid rain and atmospheric pollutant. The unique damage mechanism and route induced by salt in historical buildings made of wood were summarised; the damage was attributed to the accumulation of sulphate salt causing hydrolysis of the carbohydrates and salt crystallisation resulting in mechanical damage. This interdisciplinary study is significant for decision making in studies related to the preservation and evaluation of historical wooden buildings.
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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 | 0.000 |
| 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".