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Record W3161291919 · doi:10.3390/f12050590

Modelling the Material Resistance of Wood—Part 3: Relative Resistance in above- and in-Ground Situations—Results of a Global Survey

2021· article· en· W3161291919 on OpenAlexaff
Christian Brischke, Gry Alfredsen, Miha Humar, Elena Conti, Laurie J. Cookson, Lukas Emmerich, Per Otto Flæte, Stefania Fortino, Lesley Francis, Ulrich Hundhausen, Ilze Irbe, Morten Klamer, Davor Kržišnik, Boštjan Lesar, Eckhard Melcher, Linda Meyer-Veltrup, Jeffrey J. Morrell, J. B. S. Norton, Sabrina Palantı, Gerald Presley, Ladislav Reinprecht, Tripti Singh, Rod Stirling, Martti Venäläinen, Mats Westin, Andrew H.H Wong, Ed Suttie

Bibliographic record

VenueForests · 2021
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsCRB Innovations (Canada)FPInnovations
FundersFachagentur Nachwachsende RohstoffeMinistrstvo za Izobraževanje, Znanost in ŠportBundesministerium für Ernährung und LandwirtschaftEnergimyndighetenVetenskapsrådetAgence de l'Environnement et de la Maîtrise de l'EnergieSvenska Forskningsrådet FormasNorges ForskningsrådAgence Nationale de la RechercheVINNOVAEuropean Commission
KeywordsEnvironmental scienceDurabilityResistance (ecology)Materials scienceEcologyComposite materialBiology

Abstract

fetched live from OpenAlex

Durability-based designs with timber require reliable information about the wood properties and how they affect its performance under variable exposure conditions. This study aimed at utilizing a material resistance model (Part 2 of this publication) based on a dose–response approach for predicting the relative decay rates in above-ground situations. Laboratory and field test data were, for the first time, surveyed globally and used to determine material-specific resistance dose values, which were correlated to decay rates. In addition, laboratory indicators were used to adapt the material resistance model to in-ground exposure. The relationship between decay rates in- and above-ground, the predictive power of laboratory indicators to predict such decay rates, and a method for implementing both in a service life prediction tool, were established based on 195 hardwoods, 29 softwoods, 19 modified timbers, and 41 preservative-treated timbers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.869

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 head0.024
GPT teacher head0.222
Teacher spread0.199 · 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

Citations24
Published2021
Admission routes1
Has abstractyes

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