Labels for Eternity: Testing Printed Labels for use in Wet Collections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it