Assessment of the Surface Water Quality of Ibrahim River (Lebanon): A Spatio-Temporal Analysis
Bibliographic record
Abstract
Monitoring surface water quality offers a clear understanding of its parameters over time and space. The Ibrahim River, one of the main rivers in Lebanon, was monitored over one hydrological year, from March 2021 to April 2022. Samples were collected from seven stations in the watershed, once every two weeks. A total of 504 samples were then analyzed for pH, conductivity, turbidity, total dissolved solids, dissolved oxygen, biochemical oxygen demand, dissolved nitrate, dissolved potassium, dissolved chloride, total alkalinity, fecal coliforms, and total coliforms. Principal Component Analysis (PCA) was able to highlight two principal components (PCs), representing spatial and temporal variations, identifying areas of pollution and the influence of flow on water quality. The adapted Water Quality Index (WQI) confirmed the PCA trend with an overall average for the entire watershed of 83.70 ± 4.97, indicating a “good” water quality.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".