Predicting the Impacts of the COVID-19 Pandemic on Food Supply Chains and Their Sustainability: A Simulation Study
Bibliographic record
Abstract
The COVID-19 pandemic has continued to result in severe disruptions to food supply chains. In this research, we present a simulation study on the impact of the COVID-19 pandemic on food supply chains and their sustainability using the lobster industry in Nova Scotia, Canada, as an example. The main contributions of this paper are twofold. First, it analyzes how the pandemic has negatively disrupted lobster supply chains and their sustainability. Second, it demonstrates how a simulation-based methodology based on the software AnyLogistix can be applied to examine the effects of a pandemic on food supply chains. We show the impacts of the COVID-19 pandemic from four perspectives: production-inventory dynamics, customer performance, financial performance, and lead-time performance. Our findings include the following. First, the pandemic has created a backlog problem for the live lobster industry. Second, it has significantly increased the lead time of the lobster supply chain. Overall, this research can help the government and trade organizations to devise appropriate policies to reduce the negative impacts of the pandemic on food supply chains and their sustainability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".