PRELIMINARY WATER QUALITY ASSESSMENT USING CANADIAN WATER QUALITY INDEX OF RAS EL-AIN PONDS, SOUTH LEBANON
Bibliographic record
Abstract
Water quality deterioration in Lebanon is a pressing national issue and there is a high need for continuous water quality assessment and monitoring of water bodies in the country. This study aims at assessing water quality of Ras El-Ain Ponds, a major natural water resource for domestic use in Tyre district, South Lebanon by using the widely applied Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI). Sampling was conducted during February-March, 2023 and physiochemical and microbiological water quality parameters were determined using standardized methods. Assessed parameters in our study are temperature, turbidity, electrical conductivity (EC), total dissolved solids (TDS), pH, ammonia, nitrite, nitrate, chloride, hardness, sulfate, orthophosphate, total organic carbon, fluoride and E. coli which is considered as a main indicator of fecal contamination of waterways. CCME WQI was calculated following the Canadian Water Quality Guidelines in view of the permissible levels set by the World Health Organization (WHO), Lebanese Standards Institution (LIBNOR) and CCME. With the exception of marginal levels of EC and TDS according to WHO and CCME, assessed water quality parameters were within the permissible ranges of standards for domestic water use. As for the CCME WQI, the obtained values fell between 90.73 and 94.42 % which indicates a “Good Quality” level confirming the suitability of water for domestic use. This research presents the first attempt to evaluate the CCME WQI of Ras El-Ain Ponds for quality monitoring and proper decision making. More comprehensive validation of CCME WQI that covers temporal and climatic variabilities is recommended for the assessment process.
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How this classification was reachedexpand
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 | 0.007 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".