Analysis on Production Efficiency of Laying Hens in China—Based on the Survey Data of Five Provinces
Bibliographic record
Abstract
Problem Description: China is one of the major countries in the world of laying hens. However, compared with the United States and the European Union, the production efficiency of laying hens in China still lags far behind. Objectives: To guide the farmers to improve the effiency of laying hens breeding, we use the survey data to analysis the effiency and give advice. Methodology: This paper uses field survey data from five provinces to measure the technical efficiency of laying hens through DEA model. Key Findings: The results show that the average technical efficiency of survey households is 95.411%. Through the analysis, we found that layer chicken production technology in Hebei province is the highest, Liaoning province layer chicken production efficiency is the lowest. Implications: In the overall layout of laying hens breeding, the laying hens industry development should vigorously promote the rationalization of regional layout, give full play to regional advantages, and promote the development of laying hens 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".