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Record W1545193142 · doi:10.15376/biores.4.2.771-788

Properties of birch outer bark panels reinforced with wood strands in the surface layers

2009· article· en· W1545193142 on OpenAlexafffundabout
Roger Pedieu, Bernard Riedl, André Pichette

Bibliographic record

VenueBioResources · 2009
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité du Québec à ChicoutimiUniversité Laval
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesUniversité Laval
KeywordsBark (sound)Materials scienceComposite materialCore (optical fiber)Economic shortageCorkRaw materialPressingStructural engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

The high demand of wood as a raw material can be expected to soon lead to a severe shortage, resulting in drastic competition between various mills. This competition will be worsened by a restriction of forest cuttings in Quebec. One of the solutions to this problem would be to develop a mixed panel in which the strands of core layer are substituted by outer bark particles, and in this particular case, by particles of white birch. This type of panel could be used as siding panels and for the fabrication of boxes, bins, and commercial shelving. The objective of the present research work concerns the design, the manufacture, and the evaluation of mechanical and physical properties of this type of panel. Two manufacturing factors were taken into account: the strands orientation in the face layers and the alkali treatment made on the bark particles used in the core layer. All produced mixed panels met and exceeded almost all CAN3-0437 R-1 and O-1 property requirements. The alkali treatment of bark particles did not improve the mechanical properties of manufactured panels. The statistical analysis method that was used made it possible to choose the panel with non-oriented wood strands in the surface layers and alkali treated bark particles in the core layer as the best by taking into account only the bending strengths in both major and side axes of a panel.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.269

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 head0.018
GPT teacher head0.186
Teacher spread0.168 · 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 designBench or experimental
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

Citations2
Published2009
Admission routes3
Has abstractyes

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