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Record W1976445853 · doi:10.1520/jai101446

Towards Development of a Performance Standard for Assessing the Effectiveness of Wall-Window Interface Details to Manage Rainwater Intrusion

2009· article· en· W1976445853 on OpenAlexafffund
Michael Lacasse, S. M. Cornick, M. Z. Rousseau, M. M. Armstrong, M. Nicholls, Silvio Plescia

Bibliographic record

VenueJournal of ASTM International · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsCanada Mortgage and Housing CorporationNational Research Council Canada
FundersNational Research Council CanadaPublic Works and Government Services Canada
KeywordsProtocol (science)Rainwater harvestingWindow (computing)Computer scienceInterface (matter)IntrusionTest (biology)Set (abstract data type)SimulationReliability engineeringEngineeringOperating systemGeology

Abstract

fetched live from OpenAlex

Abstract Laboratory water spray testing identifies the performance of a component or assembly under a specified set of simulated wind-driven rain conditions. Well-developed water spray test protocols can also help identify where an assembly is vulnerable to water entry, the test loads at which water entry occurs, and whether the water entry is managed by the installation details in such a way that it does not result in within-wall damage. This paper presents a proposed laboratory test protocol for assessing the effectiveness of wall-window interface details with regard to management of rainwater and provides a rationale for a performance-based approach to the evaluation method. An overview of the test approach is provided, and details of the test apparatus and test specimen are given, including information on implementation of the test method. Examples of testing performed according to the proposed protocol are provided. Finally additional tests for evaluating the performance of installation details are suggested. The additional tests are for field evaluation of installation details and for laboratory evaluation of installation details with regard to the risk of condensation along window frames.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.013
GPT teacher head0.284
Teacher spread0.271 · 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
Published2009
Admission routes2
Has abstractyes

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