Conserving Animation Cels: Reattaching Loose Paint Without Adhesive
Bibliographic record
Abstract
A collaborative research project between the Getty Conservation Institute and the Walt Disney Animation Research Library investigated storage and conservation treatment strategies for animation cels. Animation cels are transparent plastic sheets inked on the front and painted on the reverse. Common damage observed on aged cels is cracking, loss of adhesion, dislocations, and losses of the paint. In practice, flaking paint of animation cels is often removed and repainted rather than consolidated. Being reverse painted, paint consolidation of animation cels adds another layer of complexity to established approaches to paint consolidation. One important component of this study has involved developing minimally invasive treatments for reattaching delaminating paints, which involved studying paint condition, characteristics, and properties. This paper presents recent innovations in paint reattachment that rely on the intrinsic hygroscopic properties of the cel paint formulations. The new treatment is a multi-step approach that manipulates paint properties by using precise levels of relative humidity, established within a humidity chamber, in order to reactivate the paint and reattach it. A major advantage of this method is that a consolidant is not necessary. Method development and a case study procedure are presented in depth. The method developed is effective for cel paints with the same or similar formulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".