MétaCan
Menu
Back to cohort
Record W4211111942 · doi:10.3390/f13020271

Use of Agave durangensis Bagasse Fibers in the Production of Wood-Based Medium Density Fiberboard (MDF)

2022· article· en· W4211111942 on OpenAlexaff
Oswaldo Moreno-Anguiano, Alain Cloutier, José Guadalupe Rutiaga-Quiñones, Christian Wehenkel, Rigoberto Rosales Serna, Pamela Rebolledo, Claudia E. Hernández-Pacheco, Artemio Carrillo-Parra

Bibliographic record

VenueForests · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsCegep de Trois-RivieresUniversité Laval
Fundersnot available
KeywordsBagasseEngineered woodMaterials scienceFiberboardAbsorption of waterMedium density fiberboardPulp and paper industryComposite materialAgaveYoung's modulusFlexural strengthBotanyEngineering

Abstract

fetched live from OpenAlex

There is an increasing interest in using non-wood lignocellulosic materials for the production of wood-based medium density fiberboard (MDF). Agave durangensis Gentry bagasse is a waste product produced in large quantities in the mezcal industry. This study evaluated the incorporation of A. durangensis bagasse fibers (ADBF) to elaborate MDF wood-based panels. Three types of panels with different ratios (wood fibers: bagasse fibers) were investigated. The ratios evaluated were 100:0, 90:10, and 70:30. The density profiles, water absorption, and thickness swell of the panels were determined, as well as the modulus of elasticity (MOE), modulus of rupture (MOR), and internal bond (IB), according to the ASTM D1037-06a standard. The results were compared to the ANSI A208.2-2016 standard. The effect of the addition of ADBF on the properties of the panels was analyzed. Density profiles were comparable among the three types of panels, while water absorption, thickness swelling, MOE, MOR, and IB were similar between panels with ratios of 100:0 and 90:10. Panels with 10% and 30% of ADBF meet the minimum ANSI requirements for quality grade 115. It is feasible to use up to 30% of ADBF in the manufacture of wood-based MDF panels.

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)0.001
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 score0.049
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.239
Teacher spread0.217 · 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

Citations21
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueForestsSame topicNatural Fiber Reinforced CompositesFrench-language works237,207