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Record W3117060829 · doi:10.18280/acsm.440605

Morphological Characterization of Chicken Feather Rachis, Neem Sawdust, and High Density Polyethylene (HDPE) Reinforced Composite Material

2020· article· en· W3117060829 on OpenAlexvenueno aff
Gayatri Uppalapati, Srinivasarao Gunji, Ramakrishna Malkapuram

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsHigh-density polyethyleneMaterials scienceSawdustPolyethyleneComposite materialComposite numberPlastics extrusionPelletMolding (decorative)Pulp and paper industry

Abstract

fetched live from OpenAlex

In this paper research was expanded on waste that can be recycled. There are so many different types of waste products available in our country that would ruin the environment. Three different products are considered to reduce some of the problems among them two are fibers and the other is polyethylene. Chicken feather rachis and Neem sawdust are the two different fibers and the reinforced material is high density polyethylene (HDPE). These materials are made as seven different compositions to find the ability of the sample in different characterizations. The seven compositions are 80H-10CF-10SD, 80H-5CF-15SD, 80H-15CF-5SD, 80H-20CF, 70H-30CF, 70H-30SD, 80H-20SD, and these compositions are made in CIPET, Hyderabad by using single screw extruder& strand pelletizer. These granules are prepared with the help of Injection moulding, the samples are examined by various testing to prove the ability, strength, structure of materials in individual, and combination forms. The testing conducted for the samples is that the Morphology of the sample was characterized using FESEM, XRD. It is examined that the ratios are light weight with high strength by using the testings and it gives perfect images without any cracks on the surface and no more defects among these ratios.

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 categoriesMeta-epidemiology (narrow)
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 score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.240
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.

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAnnales de Chimie Science des MatériauxSame topicNatural Fiber Reinforced CompositesFrench-language works237,207