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Record W1987254362 · doi:10.1520/jai100885

Envelope Remediation—A Case Study in Support of an Over-Cladding Approach

2008· article· en· W1987254362 on OpenAlexaff
Jared B. Lawrence, Paul G. Johnson

Bibliographic record

VenueJournal of ASTM International · 2008
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsCladding (metalworking)Environmental remediationEnvelope (radar)Materials scienceBuilding envelopeNuclear engineeringComposite materialEngineeringContaminationAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract This paper addresses a case study in which a contemporary, high-performance glass curtain wall and a composite metal panel cladding system were installed over an existing failed 1960s masonry wall with aluminum windows. In addition to consideration of the typical performance requirements for air and water infiltration and thermal insulation, the new wall systems were designed to meet glass shard retention requirements and to accommodate improvements to the structural frame required by the General Services Administration. These performance requirements were all met while creating a more inviting exterior appearance, improving the interior work environment, extending the building’s service life, and reducing energy consumption. Problems of the original design and construction, extent of previous remediation attempts, findings of the investigation of the existing building, and the development of remediation options are discussed to provide background to selection of the over-cladding approach for remediation. Opportunities and limitations posed by the existing building, LEED requirements, glass shard retention, construction strengthening, full and uninterrupted occupancy, as well as other challenges inherent in the over-cladding of an existing high-rise building on a tight urban site are identified and discussed, and positive results are identified.

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 score0.177
Threshold uncertainty score0.312

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 head0.016
GPT teacher head0.260
Teacher spread0.245 · 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
Published2008
Admission routes1
Has abstractyes

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