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Record W4229444971 · doi:10.3390/f13050722

Drying Behavior of Hardwood Components (Sapwood, Heartwood, and Bark) of Red Oak and Yellow-Poplar

2022· article· en· W4229444971 on OpenAlexaff
Sohrab Rahimi, Kaushlendra Singh, David DeVallance, Demiao Chu, Mohsen Bahmani

Bibliographic record

VenueForests · 2022
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsFPInnovationsUniversity of British Columbia
Fundersnot available
KeywordsHardwoodBark (sound)ChemistrySpecific gravityShrinkageVolume (thermodynamics)BotanyMoisturePorosityHorticultureMineralogyComposite materialMaterials scienceBiologyEcology

Abstract

fetched live from OpenAlex

This paper presents differences in the drying behavior of red oak and yellow-poplar sapwood, heartwood, and bark and their relationship with selected physical characteristics. Drying experiments were performed on samples of sapwood, heartwood, and bark of respective species at 105 °C under nitrogen conditions. In addition, physical characteristics such as green moisture content, specific gravity, volumetric shrinkage, shrinkage of the cell wall, total porosity, pore volume occupied by water, and specific pore volume were calculated. The results showed that the volumetric and cellular shrinkages of sapwood were greater than those of heartwood for both species. For red oak, the specific gravity of sapwood and heartwood was not significantly different. Additionally, the total porosity of heartwood was lower than that of sapwood in red oak. The results also indicated that yellow-poplar dried faster than red oak. Among all three components, bark dried faster than sapwood and heartwood in both species. The activation energy for sapwood drying was less than for heartwood drying.

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 score0.250
Threshold uncertainty score0.395

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 head0.014
GPT teacher head0.195
Teacher spread0.180 · 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
Published2022
Admission routes1
Has abstractyes

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