Benchmarking on Water Resource Utilization Efficiency of Prefecture-Level Cities in Jiangxi, China: A Bootstrap-DEA Approach with Three-Stage DEA Models
Bibliographic record
Abstract
China has long been adopted traditional data envelopment analysis (DEA) models to measure water resources utilization efficiency of different provinces and cities without bias correction of efficiency scores. In this study, the Bootstrap-DEA approach embedded 3-stage DEA models was introduced to analyze the comprehensive efficiency of water resources utilization in the different prefecture-level cities Jiangxi and tried to improve the application of this method for benchmarking and inter-regional research. It is found that the bias corrected efficiency scores of Bootstrap-DEA differ significantly from those of the traditional DEA model, which implies that Chinese researchers need to update their DEA models for more scientific calculation of water resources utilization efficiency scores. This research has helped narrow the inter-regional gap in the comprehensive efficiency measurement and improvement of water resources utilization. It is suggested that Bootstrap-DEA embedded 3-stage DEA models be widely applied into afterward research to measure comprehensive efficiency of water resources utilization in regional inter-city so as to better serve for efficiency improvement and related decision making.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".