Selection of Suitable Guidelines for the Application of Sewage Sludge in Agricultural Using Hierarchical Analysis System (AHP)
Bibliographic record
Abstract
Background:Sewage sludge management is one of the most challenging parts of sewage treatment in terms of economic, design and environmental issues.One of the common ways of disposing of sludge is its use in agricultural land.Due to the presence of various contaminants in sludge Sewage, its safe and effective use in agricultural land requires the development of a special Guidance.Methods: This study was carried out to survey the guidelines of different countries regarding the application of sewage sludge on the ground and prioritize them according to the native conditions of Iran using multivariate analysis and expert opinion and using the AHP method.Resultss: The United States, Australia, Canada, China, Japan, the European Union, Russia, Turkey, and South Africa have the highest rating by applying the scores of indexes and taking into account the weight of the criteria in accordance with the standards of the US Environmental Protection Agency, respectively.Conclusion: The analysis of the results of this study showed that among the various guidelines examined, the US Environmental Protection Agency (EPA) has the most executive capacity in terms of executive capacity, comprehensiveness, transparency and precision.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".