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Record W2547175130 · doi:10.1556/1848.2016.7.2.7

Vacuum insulation panels (VIPS) in building envelope constructions: An overview

2016· article· en· W2547175130 on OpenAlexaff
K.O. Song, Phalguni Mukhopadhyaya

Bibliographic record

VenueInternational Review of Applied Sciences and Engineering · 2016
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsVacuum insulated panelBuilding envelopeArchitectural engineeringEnvelope (radar)EngineeringZero emissionProduct (mathematics)Green buildingCivil engineeringMechanical engineeringThermal insulationTelecommunicationsMathematicsElectrical engineeringThermalPhysicsMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Driven by updated building energy codes and green building initiatives across the world, vacuum insulation panel, also known as VIP, has become a desired insulation product for building envelope constructions. VIP has initial center-of-panel thermal conductivity of 0.004 W/mK or lower, and integration of VIP in building envelopes can reduce CO 2 emissions and contribute towards ‘net-zero’ or ‘near-net-zero’ building constructions. Although VIPs have been applied in real-world constructions across the world, primarily in Asia, Europe and North America, it is still a novel building product under investigation. This overview paper is a summary of fundamentals, constituents, constructions and performances of VIPs. The paper shows there exists many advantages and challenges associated with the integration of VIPs in building envelope constructions. The speed at which VIPs will be integrated in building envelope construction in the coming years remains unclear; nevertheless, it is evident that vacuum technology is the promising way forward for sustainable building envelope constructions in the 21st century.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.215

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 head0.035
GPT teacher head0.295
Teacher spread0.260 · 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 designBench or experimental
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

Citations4
Published2016
Admission routes1
Has abstractyes

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