Status and trends of water quality in the Tafna catchment: a comparative study using water quality indices
Bibliographic record
Abstract
Water quality indices (WQIs) are necessary for resolving lengthy, multi-parameter, water analysis reports into single digit scores; different WQIs have been developed worldwide which are greatly differing in terms of mathematical structures, the numbers and types of variables included, etc. The aim of this paper is to evaluate trends of water quality in Tafna basin with a comparison of 10 WQIs perceived as the most important indices for water quality assessment. The results show that there is an appreciable difference between indices values for the same water sample. The results also show that water quality categorization for sampling stations in the Canadian Council of Ministers of the Environment WQI (CCMEWQI) and British Columbia WQI (BCWQI) was found to be ‘marginal’ for all sampling stations, except Hammam Boughrara reservoir and Mouillah wadi where it was found to be ‘poor’. For the Aquatic Toxicity Index, it was found to be ‘totally unsuitable for normal fish life’ for all stations and ‘suitable only for hardy fish species' for Mouillah wadi and Boughrara reservoir. The results show that this transboundary catchment always needs strategies for more effective pollution control management. Future use of WQIs in this way should prove a valuable tool for environmental planning decision-makers in tracking water quality change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".