Research on the resilience of petroleum industry chain and supply chain network from the perspective of China
Bibliographic record
Abstract
The security situation of the global petroleum industry chain and supply chain network has undergone significant changes, especially during events such as the pneumonia pandemic. As a country with significant changes in the petroleum industry and supply chain, studying China is of great significance. At the same time, the overall research on the security of the petroleum industry chain and supply chain is not yet complete. Therefore, starting from node resilience and structural resilience, this study constructs a research system for preparation, stability, resistance, and reconstruction, which can comprehensively study the security of the petroleum industry chain and supply chain. Research has found that: (1) The central countries of the petroleum industry chain and supply chain are relatively fixed, concentrated in countries such as the United States, China, the Netherlands, and Canada. (2) The petroleum industry chain and supply chain network are all heterogeneous networks , and there are significant differences in the countries in the network. (3) In the supply chain network of the petroleum industry chain, the efficiency of the network will sharply decrease before the ratio of node to edge losses reaches a certain value. (4) In the petroleum industry chain and supply chain network, countries located at the hub will prioritize recovery. Research is of great significance for maintaining the security of the petroleum industry chain and supply chain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".