Chemical fingerprinting of saline water intrusion into sewage lines
Bibliographic record
Abstract
High salinity in sewage sludge can affect not only the operation of wastewater treatment plants (WWTPs) but also the quality of treated water generated, thereby limiting its downstream reuse. Using data on geochemical parameters, both for the central WWTP in Abu Dhabi, UAE, and literature values for potential regional saline water sources (e.g., shallow groundwater and regional Arabian Gulf seawater), a variety of chemical fingerprinting diagnostic ratios were calculated and plotted in order to determine the source of salinity in the municipal sewage. Data were compared with data from a regional WWTP that was not impacted by salinity. Monitoring data demonstrated persistently elevated levels of salinity in the municipal wastewater arriving at the central WWTP from the city. Dilution/concentration analysis using a conductivity vs. chloride plot showed both potential sources, i.e. Arabian Gulf seawater and coastal hypersaline groundwater, as feasible sources of wastewater salinization. Further diagnostic analysis using a Panno Plot indicated that coastal groundwater was the only likely source of salinization of municipal sewage. Additional confirmation of the identity of the source and the extent of mixing using different lines of evidence like stable isotope ratios is recommended for future study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".