Research on Agricultural Logistics Efficiency Based on DEA and Tobit Regression Models
Bibliographic record
Abstract
As the link between production and consumption, urban and rural areas, industry and agriculture, agricultural logistics plays a vital role in optimizing the rural industrial structure and developing the rural economy. However, the efficiency of agricultural logistics has been less studied. This paper launched a study on the evaluation of agricultural logistics performance based on DEA model, constructed an input-output index system, collected and collated data on relevant indicators of agricultural logistics in 30 provinces and municipalities (excluding Tibet) in 2018 and 2019, and applied DEAP2.1 software to evaluate the efficiency, and the research results showed that 7 provinces and municipalities showed strong agricultural logistics efficiency in 2018, 5 provinces and municipalities have efficiencies between 0.8 and 1, and 18 provinces and municipalities have efficiencies below 0.8. In 2019, 7 provinces and municipalities showed strong agricultural logistics efficiency, 6 provinces and municipalities had efficiency between 0.8 and 1, and 17 provinces and municipalities had efficiency below 0.8. Based on this, a regression analysis study was conducted on the influencing factors affecting agricultural logistics efficiency through the Tobit model, and the study concluded that the level of rural goods turnover, the level of agricultural logistics operation, the level of education of the labor force is the main factor affecting the efficiency of agricultural logistics, while the regional living standard and the level of construction of transport facilities have shown a significant uncorrelated.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".