MétaCan
Menu
Back to cohort
Record W2340724644 · doi:10.1002/pen.24314

Rotomolded polyethylene‐agave fiber composites: Effect of fiber surface treatment on the mechanical properties

2016· article· en· W2340724644 on OpenAlex

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

VenuePolymer Engineering and Science · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceComposite materialMaleic anhydrideAgavePolyethyleneSilaneUltimate tensile strengthFiberFlexural strengthSodium hydroxideNatural fiberPolymerChemical engineeringCopolymer

Abstract

fetched live from OpenAlex

In this work, a comparison between different agave fiber surface treatments has been presented to improve the mechanical properties of rotomolded natural fiber composites (NFC). The fiber treatments were carried out with sodium hydroxide, 2‐chlorobenzaldehyde, maleic anhydride grafted polyethylene, acrylic acid, methyl methacrylate, and triethoxy vinyl silane. In particular, a simple dry‐blending technique was used to introduce agave fibers in the polymer matrix (linear medium density polyethylene). The samples were produced at 15 wt% fiber content and characterized in terms of morphology, density, hardness, and mechanical properties (tension, flexural, and impact). The results showed that surface treatments improved the homogeneity (uniform morphology) of NFC and the best mechanical improvements (77% for strength and 30% for stiffness) were obtained with maleic anhydride grafted polyethylene. POLYM. ENG. SCI., 56:856–865, 2016. © 2016 Society of Plastics Engineers

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 score0.002
Threshold uncertainty score0.369

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 head0.011
GPT teacher head0.215
Teacher spread0.204 · 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