Resilience theory incorporated into urban wastewater systems management. State of the art
Bibliographic record
Abstract
Government bodies, utilities, practitioners, and researchers have growing interest in the incorporation of resilience into wastewater management. Since resilience is a multidisciplinary term, it is important to review what has been achieved in the wastewater sector, and describe the future research directions for the forthcoming years. This work presents a critical review of studies that deal with resilience in the wastewater treatment sector, with a special focus on understanding how they addressed the key elements for assessing resilience, such as stressors, system properties, metrics and interventions to increase resilience. The results showed that only 17 peer-reviewed papers and 6 relevant reports, a small subset of the work in wastewater research, directly addressed resilience. The lack of consensus in the definition of resilience, and the elements of a resilience assessment, is hindering the implementation of resilience in wastewater management. To date, no framework for resilience assessment is complete, comprehensive or directly applicable to practitioners; current examples are lacking key elements (e.g. a comprehensive study of stressors, properties and metrics, examples of cases study, ability to benchmark interventions or connectivity with broader frameworks). Furthermore, resilience is seen as an additional cost or extra effort, instead of a means to overcome project uncertainty that could unlock new opportunities for investment.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".