Value-added Uses of Eggshell and Eggshell Membranes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".