Modelling the degradation of acidic and alkaline printing paper
Bibliographic record
Abstract
Abstract There has always been an interest in the professional communities of libraries, archives and conservation science to find ways of estimating the rate of degradation of paper under archival conservation conditions. Previously we reported a number of considerations for developing a kinetic degradation model based on Whatman no.1 paper. In the present research, this model was extended to 10 different papers and validated. Various physical and chemical properties of acidic, neutral, and alkaline papers were measured, such as the degree of polymerization (DP), tensile strength, equilibrium moisture content (EMC), and pH, as well as alkaline reserve when applicable. The activation energy (Ea) based on DP of cellulose and zero-span tensile strength were determined. Ea and pH had the most significant influence on the simulated decay of paper. Papers with a high Ea (> 120 kJ mol-1), alkaline such as those containing at least 2% CaCO3, and acidic –but good printing quality papers made of bleached chemical pulp– were found the most durable in ambient conditions. Papers with a lower Ea (< 110 kJ mol-1) such as lignocellulosic papers containing significant amount of mechanical pulp were much less stable over time. Whatman filter papers, used as models of pure cellulosic papers, were found to have low Ea despite the good quality cotton fibers. A generic isoperm equation based on Ea was developed to predict the changes in the state of papers under various climatic conditions, and was applicable independently of the pH of the paper. The model developed allows a better quantification of the deterioration rate of printing papers such as those that are currently, and will be in the future, found in our archival collections.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".