Comprehensive evaluation of water resources security in the Yellow River basin based on a Fuzzy Multi-Attribute Decision Analysis Approach
Bibliographic record
Abstract
Abstract. In this paper, a Fuzzy Multi-Attribute Decision Analysis Approach (FMADAA) was adopted in water resources security evaluation for the nine provinces in the Yellow River basin in 2006. A numerical approximation system and a modified left-right scoring approach were adopted to cope with the uncertainties in the acquired information. Four multi-attribute decision making methods were implemented in the evaluation model for impact evaluation, including simple weighted addition (SWA), weighted product (WP), cooperative game theory (CGT) and technique for order preference by similarity to ideal solution (TOPSIS) which could be used for helping rank the water resources security in those nine provinces as well as the criteria alternatives. Moreover, several aggregation methods including average ranking procedure, borda and copeland methods were used to integrate the ranking results. The ranking results showed that the water resources security of the entire basin is in critical, insecurity and absolute insecurity state, especially in Shanxi, Inner Mongolia and Ningxia provinces in which water resources were lower than the average quantity in China. Hence, future planning of the Yellow River basin should mainly focus on the improvement of water eco-environment status in the provinces above.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".