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Record W3157254729 · doi:10.1520/jte20190902

A Nondestructive Longitudinal Laboratory Test Method for Detection of Incipient Ultrastructural Changes in Wood

2021· article· en· W3157254729 on OpenAlexafffund
Robert Lepage, Samuel V. Glass, Paul Y. de la Bastide, Phalguni Mukhopadhyaya

Bibliographic record

VenueJournal of Testing and Evaluation · 2021
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNondestructive testingUltrastructureMaterials scienceComposite materialPhysicsBiologyBotany

Abstract

fetched live from OpenAlex

ABSTRACT The established methods for determining the effects of ultrastructural changes in structural wood members rely upon traditional, possibly outmoded, testing paradigms. These methods usually involve destructive testing of wood specimens that are exposed to environmental conditions infrequently experienced in buildings. Understanding how the ultrastructure changes with time within the same specimen is crucial for building practitioners in assessing risks to life-safety, assessing remaining service life of structural components, and aiding in the identification of mitigation or remediation measures. The primary agents causing ultrastructural modifications to wood are wood rotting basidiomycetes but may also include other biotic and abiotic agents; the methods contained herein should be applicable to all such longitudinal experiments. This article reviews existing literature on decay testing and validates a new method to assess longitudinal changes in mechanical properties with time using nondestructive test measures at relevant moisture contents. The validation testing shows this method has a good degree of repeatability and should permit the initial detection and monitoring of ultrastructure changes (e.g., decay). The method uses a repeatable, nondestructive four-point testing procedure for specimens controlled to specific moisture contents using energy dissipation as the salient performance metric. Recommendations are provided to refine this novel test method.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.242
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.048
GPT teacher head0.301
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

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

Citations1
Published2021
Admission routes2
Has abstractyes

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