Evaluation on Input-output Efficiency of Land Consolidation Project Based on DEA --- A Case Study of Land Consolidation Project in Chongyang County, Hubei Province
Bibliographic record
Abstract
This article studies four land consolidation projects in four towns of Chongyang County, Hubei Province, establishes system indexes for evaluation on input and output of land consolidation projects in all the four towns and employs DEA method to make an analysis of the relative efficiency of the projects in order to make an analysis of the actual efficiency of land consolidation, decide whether land consolidation is highly effective and point out a direction of improvement for higher land consolidation efficiency in the future. The result shows that the land consolidation in Qingshan Town and Lukou Town is ineffective and the land consolidation in Shaping Town and Baini Town is effective, with an average efficiency of 0.77. It proves that the overall efficiency of land consolidation in the four towns is at an upper-and-middle stream. Inefficiency is mainly manifested in cost of construction of a project, original equipment cost, other costs and redundancy of unpredictable costs, while increment of land use ratio, quantity of employment added per unit investment, rate of coverage of newly added green vegetation, newly added annual pure economic interests and yield rate of static investment have too low output. In order to enhance the efficiency of land consolidation, it is necessary to arrange all sorts of input in a reasonable way and pay enough attention to the output.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".