Bibliographic record
Abstract
In 2017, egg price in China has experienced a lot of ups and downs, which has had a significant impact on the laying hen farmers and the enterprises and related enterprises. In the first half of 2017, egg price fell, which has dropped to a minimum of 4.02 yuan/kg, while the profits of egg producers were impaired and the profit of egg processing enterprises declined. Starting in July, egg price recovered, breaking a price of 5 yuan/kg. Egg price rose sharply in August, reaching an average of 8.53 yuan/kg. In October, egg price began to fall, with a price of 7 yuan/kg. In November, egg price began to rise, rising to 8 yuan/kg. The sudden drop of egg price greatly affects the income and culture psychology of laying hen farmers, and influences the development of the egg industry. This study is aimed at egg price and egg price fluctuations in 2017, and get two conclusions: From January to July, due to the amount of laying hens breeding, breeding cost, information technology and the government’s environmental protection policy and terminal weak consumer spending, egg price fell sharply; egg price rebounded in August and December, and the highest price was in September and gradually steadied. At the same time, this paper analyzes the causes of egg price fluctuation from two aspects of supply and demand, and puts forward some suggestions on how to deal with the price fluctuations from the two aspects of enterprise and laying hen breeding farmers.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".