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Record W2564886587 · doi:10.2495/sdp-v12-n2-264-272

Computational Analysis of the Energy Efficiency of Stone Walls: Current Situation and Possible Improvements

2016· article· en· W2564886587 on OpenAlexvenueno aff
Jiří Maděra, Václav Kočí, Robert Černý

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicBuilding materials and conservation
Canadian institutionsnot available
FundersGrantová Agentura České Republiky
KeywordsCurrent (fluid)Environmental scienceEfficient energy useEnergy (signal processing)Computer scienceEngineering physicsEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Energy efficiency of selected envelopes of historical buildings is assessed. The historical masonry is represented by two types of sandstone walls with different thicknesses, which are retrofitted using internal thermal insulation systems preserving the original look of the external facades. All energy-related calculations are based on the results of hygrothermal analysis of the studied envelopes exposed to dynamic climatic conditions. The comparison of energy balances of non-insulated and retrofitted walls shows that the reduction of energy loss can reach up to 89%, depending on the type of the wall that is analysed. Hydrophobic and hydrophilic mineral wools exhibit the best results not only from the point of view of energy efficiency but also from that of hygric performance.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.123

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 head0.012
GPT teacher head0.238
Teacher spread0.225 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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