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Record W4205696013 · doi:10.22215/etd/2021-14652

Material Characterization of Wood in Historic Structures

2021· dissertation· en· W4205696013 on OpenAlexafffund
Natalie Smith

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCharacterization (materials science)DocumentationWorkflowDrillEngineeringConstruction engineeringKilnArchitectural engineeringCivil engineeringQuality (philosophy)Building materialStructural engineeringMechanical engineeringComputer scienceMaterials scienceWaste management

Abstract

fetched live from OpenAlex

When the need arises to evaluate a historic wood building, structural evaluation is often based on current design codes.The material properties defined in current codes are based on modern fast-grown lumber, whereas historic buildings were often constructed from old-growth wood, which has a considerably higher quality and strength.This thesis examines the relationship between resistance drill measurement and the mechanical properties of Eastern white pine.The goal of the research is to determine the viability of resistance drill testing for in-situ material characterization of historic wood structures.The case study of a historic barn is presented in this research to establish a workflow for evaluating wood structures through on-site non-destructive testing, using the experimentally-determined relationships between material properties and resistance drill measurement.The workflow includes documentation, condition assessment, material characterization, and iterative evaluation of the building's structural performance.i

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.180
Teacher spread0.175 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2021
Admission routes2
Has abstractyes

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