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Record W7125210680 · doi:10.18280/rcma.350603

Effect of Marine Ageing on Endurance Limit of Jute Fiber Composite Laminates

2025· article· W7125210680 on OpenAlexvenueno aff
Tahar Tioua, Djamel Djeghader

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2025
Typearticle
Language
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsnot available
Fundersnot available
KeywordsComposite laminatesComposite numberAgeingLimit (mathematics)Fiber

Abstract

fetched live from OpenAlex

In this work, the composites studied consist of a polyester resin matrix reinforced with 40% by mass of natural jute fibers arranged bidirectionally.Their elaboration was carried out by the contact molding method.The specimens obtained were then subjected to aging in natural seawater for different immersion times (90, 180, and 270 days).The evaluation of the mechanical properties was carried out through static bending tests, allowing to determine the breaking stress, subsequently used as a reference for the cyclic fatigue tests in three-point bending.The results obtained, analyzed using the linear Wöhler curve, highlighted a progressive decrease in the endurance limit of jute fiber reinforced polyester (JFRP) composites with the increase in marine aging time.This degradation of performance is mainly linked to the absorption of chloride and sodium ions, causing modifications in the chemical structure as well as an alteration in the composition of the material.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.023
GPT teacher head0.279
Teacher spread0.257 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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