Bibliographic record
Abstract
Forty years ago anxious archivists and librarians coined the catchphrases ‘paper decay’ and ‘acidic corrosion’ and thus brought the preservation of archival and library holdings to public attention. The quantity of threatened cultural heritage prompted scientists and technicians to develop a wide variety of mass deacidification methods. Mass deacidification serves memory institutions as a means of preserving originals and also functions as a beacon which signals that conservation must be perceived as a core task and provided with the necessary resources. In Switzerland the Federal Archives and the National Library (NL) opted for the papersave® process in 1995 which was implemented in the papersave swiss facility and available to all interested parties in Switzerland until 2022. During the years 2000-2014 the NL treated all collections intended and suitable for deacidification. The quality assurance concept included long-term monitoring of the treated holdings; over the observation period of currently seven to twenty years, the deacidification treatment proved to be sustainable and stable in 97% of cases. Mass deacidification is an intervention in the original substance and cannot be reversed or repeated. The responsible selection of holdings and deacidification methods, for which international standards and sufficient experience are now available, is of decisive importance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.088 | 0.027 |
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".