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Record W2047641465 · doi:10.15376/biores.8.3.3967-3981

An Optimal Thermo-Hydro-Mechanical Densification (THM) Process for Densifying Balsam Fir Wood

2013· article· en· W2047641465 on OpenAlex
Ling Li, Meng Gong, Dagang Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioResources · 2013
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsBalsamBrinell scaleMaterials scienceAbies balsameaComposite materialSoftwoodUltimate tensile strength

Abstract

fetched live from OpenAlex

To better utilize low-density softwood, a thermo-hydro-mechanical densification process performed in an open system was studied to enable the manufacture of densified wood with a hard surface, strong bonding, and good dimensional stability. This study was aimed at optimizing three densification parameters, i.e., compression ratio (CR), temperature, and time, for balsam fir (Abies balsamea (L.) Mill.). The Brinell surface hardness, bond strength, and thickness recovery ratio of densified fir were examined. It was found that the optimal densification parameters were a CR of 60%, a temperature of 230 ºC, and a time of 20 minutes. The surface hardness and bond strength of optimized densified fir were about 30 and 8 MPa, respectively. The thickness recovery ratio of the densified fir after a 2-hour cold water soaking and another 2-hour boiling treatment was about 10%. Because the densified fir in this study was used for indoor applications only, its thickness recovery ratio could be minimal under conditions of use.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.575

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 head0.026
GPT teacher head0.242
Teacher spread0.216 · 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