Bibliographic record
Abstract
Food is one of the most important resources for staying alive. This paper analyzes grain output fluctuations and their driving forces in China from 1978 to 2014, based on Empirical Mode Decomposition (EMD) method. These results show that there are two type cycles of cyclical fluctuation, one is 3-yearterm, and another is 8-year term. These results show that the 8-year cyclical fluctuation is the major term. Grain production’s cyclical fluctuation in 3 years was mainly influenced by yield of grain per unit area from 1978-2004 and 2007-2014, and by the area sown from 2004 to 2007. On the other hand, the longer cyclical fluctuation of 8 years is mainly affected by the yield of grain per unit area. The grain output is predicted for the next three years through the RBF neural network optimized by PSO. These results show that China’s annul grain output in the next three years will be stabilized at about 600 million tons, which may grow slowly though.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".