New Concept for Museum Storage Buildings Evaluation of Building Performance Model for Simulation of Storage
Bibliographic record
Abstract
Museums keep and protect a part of our material cultural heritage for future generations; however the museums only exhibit a little part of their collections and most of the objects are kept in storage. Unfortunately the climates of many storage rooms are not ideal for keeping the chemical and physical decay of the objects as low as possible. Museum storage buildings should be able to provide a considerable stable indoor environment in terms of temperature and relative humidity. This paper explores how to simulate and build low energy museums storage buildings, and the paper shows that it is possible to make a building of low building expenses, very low running expenses and very high quality. In addition it is described that the energy consumption is only 2% compared to normal HVAC solutions, and the 2% can be delivered by excess wind power from Danish windmills resulting in that the building is close to be CO2 neutral. The analysis shows very good agreement between simulations and measurements, meaning that the proposed methods can be used for designing museum storage buildings. The analysis also shows, that the weather conditions of previous years, affect the indoor environment of the following years.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".