Evaluation of Al-Thagher Wastewater Treatment Plant
Bibliographic record
Abstract
This study aims to evaluate the performance of the sewage treatment plant in Al-Thagher city, in the north of Basrah governorate, the southern part of Iraq. The plant’s performance was estimated based on an analysis of influent and effluent wastewater quality data that represented the monthly averages from Feb. 2017 to Dec. 2018. The results show that the values of temperature (T), pH, ammonia (NH3–N), chemical oxygen demand (COD) and biological oxygen demand (BOD) in all collected samples from the effluent of the plant met the Iraqi water quality standard (IWQS), whereas the values of electrical conductivity (EC), total dissolved solids (TDS), total suspended solids (TSS), sulfate (SO4–2), chloride (Cl–1) and phosphate (PO4–P) met the Iraqi water quality standard (IWQS) in some months and did not meet the standard in other months. The average removal efficiencies were in the following order: COD (77.12%) > BOD (77.03%) > TSS (62.26%) > NH3–N (59.99%) > PO4–P (12.42%) > Cl–1 (1.97%). The removal percentages for the remaining parameters had negative values. The Canadian Council of Ministers of the Environment water quality index (CCME WQI) value of the treated water was 51.80 and classified as “marginal.” The coefficients of determination between each parameter in influent or effluent were calculated. Finally, linear regression equations between these parameters were formulated so that the value of one parameter could be used to predict the value of a different parameter.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".