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Record W2346698103 · doi:10.5176/2301-394x_ace16.22

New Concept for Museum Storage Buildings Evaluation of Building Performance Model for Simulation of Storage

2016· article· en· W2346698103 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsFuture Earth
Fundersnot available
KeywordsComputer scienceStorage modelComputer data storageArchitectural engineeringDatabaseEngineeringOperating system

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.291
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it