Secondary effluent treatment by slow sand filters: performance and risk analysis
Bibliographic record
Abstract
The objective of this study is to examine the reuse of wastewater for beneficial purposes. To accomplish this objective, the efficiency of slow sand filters in removing total coliforms (TC) was studied using a probabilistic method, Three pilot scale slow sand filters were constructed at Alkhobar wastewater treatment plant, Dhahran, Saudi Arabia, The removal efficiency of filters was estimated under different operating control parameters, which included filtration rate (q), sand bed depth (d) and sand grain size (c), The Type III extreme value distribution best fitted the removal efficiency data, A multiple linear regression analysis was performed to develop a relationship for mean removal efficiency as a function of control parameters, The predicted mean response and experimental resultsof previous studies were compared to validate the empirical regression model. The control parameters and influent concentrations of total coliform were used in Monte Carlo (MC) simulations for calculating the reliability index ({J), The reliability index and corresponding risk were calculated for log normally distributed safety margins (SM), An effluent standard of 100 total coliform/100 mL was defined as capacity of the filter to ascertain the risks of exceedence, which was approximately less than 50 for 95% of the time, Pre and/or post disinfection would be necessary to meet the stipulated effluent standards for unrestricted agriculture use.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".