Short-Term Fluctuations and Risk Evaluation of Vegetables Market Price in China
Bibliographic record
Abstract
【Objective】 Taking the monthly wholesale prices of 11 kinds of vegetables in China as the object of study,the intensity and pattern of short term fluctuation of varieties of vegetables were compared and analyzed,the yield distribution of vegetables market was estimated,and the market risk of short term fluctuation was evaluated.【Method】 The study mainly adopted three methods of variation coefficient,hierarchical cluster and kernel density estimation to analyze short term fluctuation and yield distribution of vegetables market.【Result】 The results are as follows: the descending order of fluctuating intensity of vegetables is eggplant,green pepper,hot pepper,garlic,cucumber,bean,spinach,rape,tomatoes,celery and cabbage.There are three patterns of short term fluctuation which are distinct wave peak and trough pattern,narrow and compact wave pattern and fluctuating cluster pattern.The yield distribution of vegetables market is asymmetric and the probability of rising of vegetables market price is higher than that of falling.The risk of spinach market price is the greatest and its probability of the rising and falling range over 30% reaches 40.48%.Following behind spinach is eggplant.The risk of garlic market price is the smallest and its probability of the rising and falling range over 30% is only 1.82%.【Conclusion】 The overall risk of vegetables market price is high under the condition of market economy.A majority of varieties of vegetables market price are often seen ups and downs.However,the occurrence of great fluctuation of garlic market price is a small probability event.If it happened there was a reason to judge for abnormal fluctuations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".