Status and Development Proposals of Structure Lipid Industry in China
Bibliographic record
Abstract
The three macronutrients of lipid, protein and carbohydrate are closely related to human health, and their supply is basically sufficient. However, with the increasing number of patients suffered from hyperlipidemia, obesity, etc., as well as the deepening of the aging process, traditional nutrients can not fulfill the nutritional needs of these people anymore. Therefore, nutritional, processing and organoleptic properties of these three major nutrients need to be enhanced by the deep-processing. Nowadays, the recombinant protein and carbohydrate industries have made great progress in China, but the recombinant lipid industry is just in its infancy. Hence, this paper briefly introduced the status, development opportunities and challenges of domestic structure lipid industry，and expounded the underlying efficacy mechanisms of several main structure lipids. The latest research progress on the health effects and preparation technology of structure lipid were also presented. In addition, in view of the existing problems, future development advices are proposed from the perspectives of theoretical breakthrough, technological innovation, and industrial upgrading, aiming to provide valuable references for the development of domestic structure industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".