A System Dynamics Approach to Valorize Overripe Figs in the Brewing of Artisanal Beer
Bibliographic record
Abstract
Craft beer production has grown extensively worldwide. The variety of products and grains that can be used in production make this artisanal product unique. In this study, we propose a system dynamics model that allows for the evaluation of different production scenarios in which figs are used as the main ingredient. This research is inspired by the real case of small fig producers in Valle del Mayo in Navojoa, Sonora, Mexico, who, in 2020, took on the challenge of creating a processing factory for fig-derived products. This paper presents the development and application of a system dynamics approach to model the entire supply chain of overripe figs, i.e., figs that cannot be marketed in prime quality but can still be used in the production of derivative products. The method used for its development encompasses the following stages: (1) defining the craft beer supply chain variables; (2) elaborating on causal diagrams; (3) producing model stock and flow diagrams; (4) model validation; (5) sensitivity analysis and scenario evaluations; and (6) building a graphical user interface (GUI). The proposed model allows managers to assess several production policies under various assumptions of capacity and beer demand, demonstrating its value as an effective tool for strategic decision making.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".