Resilience theory incorporated into urban wastewater systems management. State of the art
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it