Comparative Research on Performance of Feed Companies in China---Based on an OR-DEAE Matrix
Bibliographic record
Abstract
Feed manufacturing play a key role in agriculture development and economic growth. The present paper elucidates an operating revenue-DEA efficiency (OR-DEAE) matrix to evaluate both big and strong aspects of feed enterprises in China based on the evaluation measure of Fortune Global 500 and a Data Envelopment Analysis (DEA) method, i.e., super-efficiency CCR model, in order to achieve a healthy and sustainable development in feed manufacturing. Results show that there is a big difference between the performances of feed companies in China. Most of the feed companies are in the question mark quadrant or dog quadrant of the OR-DEAE matrix, showing that their operation levels are relatively low. Only two benchmarking companies were in the star quadrant which reflects they have both big and strong capability. We therefore provide several suggestions for the backward companies to improve their performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".