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Bibliographic record
Abstract
China Opens Rapeseed Oil, Rapeseed Meal, and Peanut Kernel Futures and Options to Overseas Traders China Opens Rapeseed Oil, Rapeseed Meal, and Peanut Kernel Futures and Options to Overseas Traders (Yicai Global) Jan. 12 -- China has given overseas traders access to futures and options on edible oil and oilseeds less than a month after opening up the country’s soybean derivatives market. Foreign traders can deal in rapeseed oil, rapeseed meal, and peanut kernel futures and options contracts on Zhengzhou Commodity Exchange from today. US dollars can be used as margins. The move will improve the quality of futures market operations, meet the hedging needs of companies, and safeguard the security of domestic oil and oilseed supply, the person in charge of the exchange said. Market participants believe it can attract overseas traders because of good liquidity and a fair and transparent market environment, gradually forming an international trade model with Chinese futures as a price reference that enhances their influence at the global level. China began promoting the internationalization of futures in 2018. Since then, a wide range of commodities, including crude oil, pure terephthalic acid, and iron ore, have been made accessible to overseas traders. After four years of steady operation, the country has a mature opening model and systems and rules for trading, settlement, and risk control. China is the world's second-largest producer, importer, and consumer of rapeseed oil, rapeseed meal, and the largest for peanuts. From 2021 to 2022, its production and consumption of rapeseed oil and rapeseed meal accounted for more than 20 percent of the global total, while that of peanuts made up over 35 percent.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.287 | 0.131 |
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".