Most common sources of pollutants in GLAMs & Assessment of main challenges for an efficient IAQ control:Deliverable 4.1
Bibliographic record
Abstract
Volatile organic compounds (VOCs), nitrogen oxides (NOx), hydrogen sulfide (H2S), and other gases presentin the indoor air of Galleries, Libraries, Archives, and Museums (GLAMs) pose significant threat to the longterm preservation of Cultural Heritage (CH) artefacts and collections. Primarily based on literature, D4.1gathers up-to-date information on the most common sources of pollutants in GLAMs, their impact on theartefacts, and their monitoring/analysis.The state-of-the-art scientific knowledge on these pollutants concentration indoors, their known impact onartefacts, and the recommendations for acceptable concentrations are summarized in the first part of thereport. A comprehensive survey conducted by WP4 within a large group of indoor air quality (IAQ) specialistsin GLAMs sheds light on the current recommendations and guidelines concerning key airborne pollutantsin GLAMs. It aims at providing an objective assessment of the main barriers encountered for an efficient IAQcontrol in GLAMs. Besides pollutants, temperature (T) and relative humidity (RH) also have a considerableimpact on the preservation, yet new trends are emerging in the CH conservation community towards arelaxation of the strict T/RH benchmark values, in view of most needed energy savings and low carbonfootprint, while preserving the IAQ. These new trends are investigated as well.The second part of the report gathers the available sorbent materials and technologies (passive and active)on the market and used in GLAMs for IAQ control. Monitoring solutions for IAQ are varied (sensors,dosimeters, colour strips and tests), and their pros and cons are given in the third part of the report. Thisreport will provide the SIMIACCI consortium with the current perception of IAQ management and mitigationin GLAMs. It will also provide input to other work packages of the project to adjust the exploitation strategyand the communication actions to the targeted groups of interest.
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How this classification was reachedexpand
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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".