Resilience analysis of global agricultural trade
Bibliographic record
Abstract
This study examined the transport network of global marine dry bulk carriers for agricultural trade during the period from 2018 to 2021. Firstly, the resilience of agricultural trade network is noteworthy throughout the COVID-19 pandemic. Agricultural trade initially plunged by 10.15% from 2019 to 2020 and bounced by a remarkable 11.45% in 2021, ultimately restoring trade volumes to the average level observed in the pre-pandemic year of 2019. However, the ports in Brazil and Argentina displayed less resilience in their agricultural trade with a continued decline in agricultural trade quantities in 2021. Additionally, the outbound trips increased in Ukraine, Canada, and Russia and decreased in Brazil and Argentina, leading to a more tightly knit agricultural network since 2020. Overall, this study provided evidence in comprehensively assessing the capacity and resilience of global food supply chains, especially in the context of constantly evolving circumstances and challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".