Bibliographic record
Abstract
Abstract In a disaster event involving water, the length of time that collection materials remain wet is a critical factor in determining whether a successful recovery is possible. There is a substantial knowledge base for the water immersion resistance of traditional information carriers such as paper documents but not for electronic storage media. In this study, floppy diskettes, VHS videotapes, optical discs, and flash media, were immersed in different water baths for various time intervals. The baths consisted of two different sources of tap water, a more corrosive tap water solution, and artificial seawater. Most electronic media survived well in clean tap water but problems occurred with some CDs and VHS tapes. More damage became evident with electronic storage media when the immersion bath was more corrosive. For some media, such as optical discs, whether damage occurred was likely linked to the manufacturing quality of the media. Drying methods were also explored. Air-drying was determined to be the best method for recovering wet electronic storage media. Freeze-thaw-air drying and vacuum freeze-drying methods can destroy CDs and DVDs and should be avoided. If wet materials cannot be recovered and dried promptly, it was established that storage in cool water reduces deterioration significantly.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".