Occupational exposure of librarians to mold spores and metal particles: a real-time case study
Bibliographic record
Abstract
Abstract In a university library, activities typical of librarians, such as unpacking boxes, removing books from the shelves, dusting, packing boxes, cataloguing, conservation and repair, photocopying, setting materials for class, and walking in a main hallway, were found to reduce indoor air quality through the production of airborne mold spores, metal dusts, and particulate matter of different size bins. Analyzing three libraries, the activity of conservation generated 9000 spores/m3 of mold spores, nearing the exposure limit of 104 spores/m3 reported for Penicillium sp., and silver nanoparticles of about 15 µg/m3, that over 8 h would exceed the occupational exposure limit of 0.19 µg/m3. For none of the activities, the levels of particulate matter with a diameter lower than 2.5 µm (PM2.5) and 10 µm (PM10) did not exceed the 8-h time weight average limits of exposure. However, by analyzing the 5th percentile of the real-time PM10 data, values of about 1.5 mg/m3, which is concerning for occupational exposure. Measuring real-time exposures of PM could generate an estimation of levels of mold spores and metal dusts in libraries. We demonstrate a high Spearman’s rank correlation (0.70) between the 5th percentile of PM with a diameter lower than 1 µm and different metals. A high linearity (R2 = 0.85) is obtained between the total average of PM with a diameter lower than 10 µm and mold spores/m3.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".