How to Turn Poultry Manure into Valuable Resources: A Circular Business Model for Resilient and Sustainable Small and Medium-Sized Farms
Bibliographic record
Abstract
This paper illustrates how small and medium-sized farmers can resolve the complex issue of poultry manure disposal by implementing an innovative technology with the aims of reducing emissions and waste and transforming manure into precious resources for the production of energy and fertilizers. After a literature review, a case study is analyzed to identify the main elements of a circular business model that can realize a strategic priority, such as defining production and consumption processes compatible with sustainability, circularity, and resilience. This paper identifies the main elements that constitute the “value proposition,” “value creation and delivery,” and “value captured,” showing the potential benefits in terms of competitiveness and profitability. This good practice may be replicated by other breeding and agricultural companies that want to be sustainable and resilient. The analyzed topic is a key concern given the great quantity of energy and chemical substances used by farms and the challenges posed by current dramatic events, such as the Russia–Ukraine conflict and the COVID-19 pandemic, which have led to less availability of energy and fertilizers and unsustainable prices.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".