Physico‐chemical characterization of littoral water of Lake Kivu (Southern basin, Central Africa) and use of water quality index to assess their anthropogenic disturbances
Bibliographic record
Abstract
Abstract Freshwater ecosystems provide many services such as moderation of the local microclimate and a source of water and food for riparian communities. It is also a preferred habitat for many organisms such as plankton, benthic macroinvertebrates and fish. However, these ecosystems are among the most affected by various anthropogenic threats that modify water quality and ecological processes, thus affecting biodiversity. The objective of this study was the spatio‐temporal characterization of physico‐chemical littoral water parameters and the assessment of anthropogenic disturbance on the littoral zone by a water quality index (WQI). Physico‐chemical water quality data including temperature, dissolved oxygen, conductivity, pH, TDS, turbidity, SiO 2 , PO 4 3− , NO 2 − , and NH 4 + were collected from January to December 2018. They were used to calculate the WQI to assess water quality according to aquatic life, using limits values of Canadian Council of Ministers of the Environment (CCME), United States Environmental Protection Agency (USEPA) and Australian and New Zeland Environment and Conservation Council (ANZECC). PO 4 3− is out of range in all the stations while NO 2 − and Turbidity are out of range in some of the anthropized stations according to ANZECC, CCME, and USEPA recommended values for aquatic life. The WQI values range from medium to good and the high WQI values obtained in the non‐anthropized stations that reflect the negative influence of human disturbance on water quality in the Lake littoral zone. The results suggest the need for an integrated lake watershed management system in order to maintain the ecological functions of the lake and support livelihoods from the lake.
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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 | 0.000 | 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 | 0.000 | 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 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".