MétaCan
Menu
Back to cohort

Deciphering the Plasticizers for the Development of Polysaccharide basedBiodegradable Edible Coatings

2022· article· en· W4296523870 on OpenAlex
Vikram Kumar, Sudarshan Singh Lakhawat, Pushpender Kumar Sharma, Sunil Kumar, Aishwarya Pandey

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

VenueCurrent Nutrition & Food Science · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPlasticizerShelf lifeCoatingMoistureFood scienceEnvironmental scienceMaterials scienceChemistryNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Abstract: There is persistently a high demand for fresh fruits and vegetables all over the world. One of the crucial factors that reduces the shelf life of fruits and vegetables is temperature- dependent oxidation during transportation and long storage. Fruits and vegetables coating using eco-friendly coatings hold great advantage over the other synthetic coating materials. The fruits and vegetables coated with coating can prevent from rapid oxidation even at warm temperatures. It enhances the quality and shelf life and maintain the nutritional properties. Though, edible coatings prove to be beneficial, the major drawbacks associated with it is the vulnerability towards moisture- dependent rapid degradation of these fruits and vegetables. Use of appropriate plasticizers would be helpful in enhancing the moisture and oxidation resistance. The current review article will highlight the use of various plasticizers used with polysaccharide-based coatings.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.054
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.043
GPT teacher head0.286
Teacher spread0.242 · 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