Beneficial effect of gelatin on iron gall ink corrosion
Bibliographic record
Abstract
Abstract Iron gall Inks corrosion causes paper degradation (browning, embrittlement) and treatments were developed to tackle this issue. They often include resizing with gelatin to reinforce the paper and its cellulosic fibers (of diameter approx. 10 µm). This work aimed at measuring the distribution of ink components at the scale of individual paper fibers so as to give a better understanding of the impact of gelatin (re-)sizing on iron gall ink corrosion. For this purpose, scanning transmission X-ray microscopy (STXM) was used at the Canadian light source synchrotron (CLS, Saskatoon). This technique combines nano-scale mapping (resolution of 30 nm) and near edge X-ray absorption fine structure (NEXAFS) analysis. Fe L-edge measurements enabled to map iron distribution and to locate iron(II) and iron(III) rich areas. N K-edge measurement made it possible to map gelatin distribution. C K-edge measurements allowed mapping and discrimination of cellulose, gallic acid, iron gall ink precipitate and gelatin. Three fibers were studied: an inked fiber with no size, a sized fiber that was afterwards inked and an inked fiber sprayed with gelatin. Analysis of gelatin and ink ingredients distribution indicated a lower amount of iron inside the treated cellulosic fiber, which may explain the beneficial effect of gelatin on iron gall ink corrosion.
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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 | 0.000 | 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) | 0.002 | 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".