Eco-friendly innovations for enhancing value from farm to function using poultry feathers
Bibliographic record
Abstract
The poultry industry has seen significant growth in response to the expanding global population, resulting in an increased demand for white meat. Recently, agricultural by-products derived from poultry have gained attention for their potential use in various applications. However, the large-scale utilization of by-products like keratin faces challenges such as low thermoplasticity and difficulty in dissolving keratin, as well as limited knowledge about the properties and processability of the resulting products. To address these issues, both chemical and biological methods are employed to extract keratin and produce different products. One promising strategy is the bioconversion of these by-products into valuable materials using enzymes. A small peptide product obtained through enzymatic biodegradation can be used in green biotechnology for industrial applications as a feed additive or a significant protein source. This review aims to discuss the eco-friendly fermentation process for feather by-products and their multifunctional applications in areas such as water purification, cosmetics, and biomedical uses. Additionally, it addresses the challenges associated with the utilization of chicken feathers and proposes possible solutions to overcome them. • Adding value to agro-industrial waste can be accomplished through bioconversion. • Byproduct utilization is hindered by nutritional, technological, and economic obstacles. • Chicken feathers are a huge bioresource for developing multifunctional materials. • Keratin-derived materials hold promise to replace petro-based materials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".