EVALUATION OF AL KABEER AL SHAMALI RIVER'S WATER SUITABILITY FOR DRINKING, BASED ON MODELING AND PSEUDOMONAS AERUGINOSA DETECTION IN SYRIA
Bibliographic record
Abstract
Using river water as a source for drinking water is a big challenge. The aim of this study was to determine the fate of Pseudomonas aeruginosa "opportunistic bacteria " in Al Kabeer Al Shamali River and its correlation with drinking water quality (pH, Electrical conductivity, Turbidity, NO3-, NO2-, NH4+ and PO4-3) in three locations; lake inlet " Ghammam Bridge ", artificial (the 16th Tishreen Dam Lake, and the outlet Ain al-Bayda irrigation tunnel). during the period 2018-2019 and 2019-2020. The membrane filtration technique was used to detect Ps. Aeruginosa. Results showed significant differences (p<0.05) between sites for Ps. Aeruginosa, EC, Tur and NO3- . The highest counts for Ps. aeruginosa recorded in Ghammam Bridge water reaching 290000 cfu/100 mL in Jun and correlated with NH4+ (r =0.72). Then counts decreased to 420 cfu/100 ml in May in 16 Tishreen Dam Lake water, due to stratification and environmental stresses. Ps. Aeruginosa counts were higher in lake outlet than 16th Dam Lake and reached 53000 cfu/ 100 ml in July, May due to the release from sediments. Canadian Water Quality Index CCME WQI showed that the lake inlet water is moderate class C (64). The waters of 16th Dam Lake and the outlet are of good grade II B due to sedimentation. These results provide the basis for choosing appropriate sterilization methods to reach health goals to connect this major water source to the drinking water network in Lattakia city
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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 | 0.000 | 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".