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Record W2943445284 · doi:10.1039/9781788013833-00359

Value-added Uses of Eggshell and Eggshell Membranes

2019· book-chapter· en· W2943445284 on OpenAlexaff
Tamer A. Ahmed, Garima Kulshreshtha, Maxwell T. Hincke

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEggshellEggshell membranePulp and paper industryCalcium carbonateChemistryMembraneFood scienceWaste managementMaterials scienceChemical engineeringBiochemistryBiologyOrganic chemistryEngineeringEcology

Abstract

fetched live from OpenAlex

The chicken egg is a crucial source of high-quality human nutrition. Massive numbers of eggs are produced annually with a significant proportion (30%) being processed in industrial breaking plants, leading to the accumulation of eggshell (ES) and eggshell membrane (ESM) waste. ES and ESM byproducts generated by such facilities are often disposed of in landfills without pretreatment, which is not a green strategy. ES is the calcareous outer layer that is lined by the fibrous ESM; together they constitute around 10% of the egg weight. ES can be utilized for various applications as a source of calcium carbonate (CaCO3), calcium oxide, and, after transformation, hydroxyapatite. ES applications include food supplements, adsorbents, antimicrobial agents, soil amendments, catalysis, guided tissue regeneration (GTR), and CaCO3-based interventions. ESM is suitable for different applications in various formats, including intact, powdered, solubilized, and after digestion/hydrolysis. ESM-based applications include adsorption, chemical processing support, biosensors, and electrochemical cell production, along with medical technology, cosmetics, GTR, and drug mucoadhesion testing. Finally, ESM has been evaluated as an antibacterial, anti-inflammatory, antioxidant, and food supplement. The functional and structural characteristics of ES and ESM are the basis for a variety of value-added commercial products that are available or under development.

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.335
Threshold uncertainty score1.000

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 integrity0.0010.000
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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
GenreOther

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

Citations18
Published2019
Admission routes1
Has abstractyes

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