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Scheffer index as preferred method to define decay risk zones for above ground wood in building codes

2011· article· en· W2109275442 on OpenAlexafffundabout
Paul Morris, J Wang

Bibliographic record

VenueInternational Wood Products Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsFPInnovations
FundersFPInnovations
KeywordsIndex (typography)Environmental scienceMoistureGround levelPrecipitationPreservativeMeteorologyEngineeringCivil engineeringGeographyGround floorComputer scienceChemistry

Abstract

fetched live from OpenAlex

Building codes and wood preservation standards are gradually taking more account of variations in climate within and across national boundaries. In Canada, the NRC-IRC Moisture Index (MI) has been used to delineate the boundary of zones where above ground wood exposed to precipitation or conducive to moisture accumulation needs to be preservative treated to Canadian Standards Association wood preservation standards. However, the older Scheffer Index is more widely recognised in wood science circles. Above ground field test data were reviewed for experiments where matched material had been exposed at more than one test site for a sufficient period for decay to occur. The relative condition of this material at two sites was compared to the Scheffer Index values for the sites and whether the MI values were below or above 1·0. The Scheffer Index was found to be a more reliable predictor of decay condition for above ground outdoor wood applications.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.040
GPT teacher head0.286
Teacher spread0.246 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations22
Published2011
Admission routes3
Has abstractyes

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Same venueInternational Wood Products JournalSame topicWood Treatment and PropertiesFrench-language works237,207