Fuzzy Comprehensive Assessment Method Based on the Entropy Weight Method and Its Application in the Water Environmental Safety Evaluation of the Heshangshan Drinking Water Source Area, Three Gorges Reservoir Area, China
Bibliographic record
Abstract
The safety of drinking water from source areas is an important issue, and the fuzzy comprehensive assessment method is a useful evaluation approach. However, it has limitations due to its complicated calculation, as well as the effects of subjective factors on the results. The objective of the research is to develop an effective method with more objective results for tackling water environmental evaluation problems in drinking water source areas. In this study, a new method— i.e., the fuzzy comprehensive assessment method based on the entropy weight method—was proposed; a water environmental safety evaluation index system was built, and then the water environmental safety of the Heshangshan drinking water source area was evaluated. The results indicated that the water environment of the study area was substantially safe. Furthermore, water-saving measurements should be taken, the industrial structure should be optimized, investment in environmental protection should be increased, and the utilization ratio of water resources should be improved. It can be concluded that the proposed approaches were feasible and reasonable. It is the first attempt to develop such an evaluation method and index system for water environmental safety evaluation, which can provide references and decision support for the related researchers and managers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".