Towards cellular agriculture: An exploratory supply chain model
Bibliographic record
Abstract
Cellular agriculture, which uses biotechnology to produce animal-derived products, has been identified as a possible solution to reduce the negative environmental impacts associated with traditional meat and dairy production. However, available life cycle and techno-economic assessments for cultured meat production suggest that additional environmental and cost improvements are needed to compete with traditional meat production methods. The adoption of circular supply chains has been found to improve the economic and environmental outcomes of production processes. The use of agricultural and food byproducts, such as hydrolyzed soymeal, as a source of amino acids has been identified as a way to reduce cost and environmental impacts. However, the impact of these undefined sources on cell production efficiency is largely unknown. The aim of this paper is to develop a novel exploratory supply chain model for a viable large-scale cellular agriculture network that considers facility location, ingredient blending, capacity design and technology selection problems. A bi-objective mixed integer linear programming model is developed to investigate the dynamics between demand, capacity design, location, ingredient blending and technology selection decisions as well as trade offs when optimizing for cost versus carbon emissions. Useful managerial insights are developed through various computational experiments, including modeling supply chain network design under deterministic and stochastic demand and the development of iso-cost curves to help decision makers design the optimal blending of chemically undefined byproduct ingredients with pure pharmaceutically sourced ingredients. • Cellular agriculture makes meat and dairy products via biotechnological processes. • Cell Ag has potential to reduce the environmental impact of meat/dairy production. • An exploratory supply chain model for a large-scale Cell Ag network is developed. • Addresses location–allocation, blending, capacity, tech selection/sizing decisions. • Dynamics and trade-offs of design factors, costs and carbon emissions are studied.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".