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Record W2907616816 · doi:10.15376/biores.14.1.401-408

Balsam fir strength behavior at moisture content in service after freezing in green condition

2018· article· en· W2907616816 on OpenAlexafffund
Claudia B. Cáceres, Roger E. Hernández

Bibliographic record

VenueBioResources · 2018
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAbies balsameaBalsamMaterials scienceWater contentComposite materialRelative humidityCompressive strengthHumidityFlexural strengthSoftwoodEquilibrium moisture contentPulp and paper industryAnimal scienceGeotechnical engineeringChemistryMeteorologyBiology

Abstract

fetched live from OpenAlex

In northern countries, engineered wood products are widely used for the construction industry. Recently, a concern has been raised regarding a slow freezing rate in green wood (of the order of 1 °C / h up to 25 °C) that could greatly reduce wood strength. Thus, logs processed during winter might produce lumber with a lower mechanical strength in service than those produced during summer. Therefore, the main objective of this study was to evaluate the influence of the freezing rate on the selected mechanical properties of balsam fir (Abies balsamea [L.] Mill). Three groups of 17 planks (20 mm x 20 mm x 1200 mm) were exposed to three freezing rates (10 °C/h, 5 °C/h, 1 °C/h) and another group was kept as a control group. After the freezing treatments, the planks were stored in a conditioning room at 20 °C and 65% relative humidity to obtain an equilibrium moisture content of 14%. Samples of 10 mm x 10 mm x 190 mm and 10 mm x 10 mm x 40 mm, for static bending and parallel compression to the grain tests were then prepared, respectively. The results showed that the freezing rate did not have any effect on the mechanical strength of balsam fir wood.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.974

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 head0.022
GPT teacher head0.211
Teacher spread0.189 · 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

Citations8
Published2018
Admission routes2
Has abstractyes

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