Wood-water relationship and micro-chemical properties of huminated archaeological European elm (Ulmus laevis P.)
Bibliographic record
Abstract
Abstract Archaeological wood requires effective conservation to prevent further degradation, and traditional modifications such as polyethylene glycol (PEG) have limitations, including hygroscopicity and chemical degradation over time. To explore alternative modification, this study was conducted to investigate the suitability of humins, crosslinked with different concentrations of succinic acid (SA), to protect archaeological elm wood from the Agapia Monastery against water and to determine the modification mechanism. Key parameters such as dimensional stability, moisture sorption isotherms, and determination of accessible hydroxy groups as a function of humination modification were analyzed using dynamic vapor sorption (DVS). The modification mechanism was studied by microstructural and chemical properties evaluation by Confocal-Raman spectroscopy and scanning electron microscopy (SEM). Results indicated that humins, particularly crosslinked with SA, significantly improved the water-related properties of wood and its dimensional stability. The humination also reduced the accessibility of the hydroxy group, thus decreasing the equilibrium moisture content (EMC) of huminated elm at relative humidities (RHs) ranging from 0 to 95%. While SEM images revealed structural changes in the modified wood, Confocal-Raman spectroscopy confirmed the successful allocation of humins into the cell walls. This study demonstrates that humins are promising materials for archaeological wood conservation, providing improvements in both chemical and physical properties.
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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".