Bibliographic record
Abstract
Although the global food system has been tempered for many years, but recent events have shown us that our global food system is unstable even in the parts of the world that it generally serves well. So, researching out a better food system has great significance to humanity. First of all, this article collects various data on agricultural production in countries around the world, according to the data type, the food system is divided into four first-level indicators, using the comprehensive model of entropy weight method and analytic hierarchy process to establish evaluation models. Secondly, the article establishes a sub-model based on four first-level indicators for facilitately analysising problems. Due to the complexity of studying the global food system, this article focuses on local areas firstly. Multi-objective optimization of the two goals of equity and sustainability according to the established sub-model, observing the changes of the other two first-level indicators, can get the conclusions: the optimization will reduce the number of food scarcity people in the world. But it will also reduce the profit and efficiency of the food system, and the selling price of various crops may rise. From the variation of sub model parameters，the new food system is more stable than the original food system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".