Performance management of small water treatment plant operations: a decision support system
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
Abstract A decision support system (DSS) is developed to optimise the performance of different operations of small water treatment systems to improve day‐to‐day decisions. The support system includes a data management system, knowledge‐based system, performance assessment of different unit processes, fault tree analyses, preventive and corrective actions and event tree analysis (ETA). Performance assessment identifies the critical events (failures) and fault tree analysis identifies the interrelationships between the critical events and the root causes. Fault trees are developed based on the information obtained from events of waterborne outbreaks, responses to questionnaires by the participating smaller utilities, state‐of‐the‐art literature review and personal communication with the operators. ETA is used to identify the potential health outcomes which are further integrated with the quantitative microbial risk assessment. The developed DSS is advanced to an automated user friendly program that can be used by treatment plant operators to assess system performance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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