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Record W4328138264 · doi:10.18280/acsm.470101

Possibility of Acacia Mangium Tree Branches as Particleboard Material

2023· article· en· W4328138264 on OpenAlexvenueno aff
Grace Siska, Lies Indrayanti, Cecep Muhlisin, Ajun Junaedi, Herianto Herianto

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2023
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsAcacia mangiumTree (set theory)AcaciaAgroforestryBotanyEnvironmental scienceBiologyMathematicsCombinatorics

Abstract

fetched live from OpenAlex

The quality of particleboard made from broad-leaved acacia branches and PVAc adhesive was investigated.A non-factorial completely randomized design (CRD) with adhesive contents of 10%, 15%, and 20% was employed.The parameters for testing physical properties included moisture content, density, water absorption, and thickness swelling.The parameters for testing the mechanical properties were modulus of elasticity, modulus of rupture, internal bonding, and screw withdrawal resistance.The results indicates that the concentration of PVAc adhesive has a significant effect on both physical and mechanical properties of the fabricated particleboards.The LSD further test indicates that particleboard with 20% adhesive content has the highest value in strength tests and meets the Indonesian National Standard (Standard Nasional Indonesia/SNI) 03-2105-2006.It is suitable for non-structural indoor (interior) element.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.032
GPT teacher head0.298
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2023
Admission routes1
Has abstractyes

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