Labels for Eternity: Testing Printed Labels for use in Wet Collections
Bibliographic record
Abstract
Abstract Will printed labels survive prolonged immersion in collection fluids, and, if so, which printing system is preferable: inkjet, laser, or thermal transfer printing? In a world with a wide variety of printers, printing substrates, and printer technologies, the interactions between them very likely affect long-term label preservation in the chemical environment of the preservation fluid. In fluid-preserved collections, the main issues frequently encountered with labels include delamination, abrasion, fading, and disintegration during immersion in solutions such as ethanol and formaldehyde aqueous solution (widely known under the commercial name formalin). Very few publications have presented testing procedures assessing the behavior and stability of printed matter immersed in the types of solvents used in fluid-based collections. This article presents a series of experiments set up at the National Natural History Collections at the Hebrew University of Jerusalem to test a variety of museum labels. The tests compared labels actually used in different natural history collections and included labels from both thermal transfer and inkjet printers. All were subjected to accelerated aging and mechanical abrasion. In our series of tests, inkjet labels gave the best performance.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".