Determination of Surface Water Quality Status and Identifying Potential Pollution Sources of Lake Tana: Particular Emphasis on the Lake Boundary of Bahirdar City, Amhara Region, North West Ethiopia, 2013
Bibliographic record
Abstract
Background: The water quality of Lake Tana is influenced by environmental stress and anthropogenic activities.Point and non-point sources are the major factors which affect the quality of the lake.Objectives: To determine surface water quality status of Lake Tana and to identify the potential pollution sources bound to Bahirdar City Administration, Ethiopia.Methodology: Laboratory based cross -sectional study was conducted in order to assess the quality and to identify the potential pollution sources of the lake.Geo referenced water samples were collected at eight sampling stations.Repeated water samples were collected and analyzed.Result: The common water quality monitoring parameter were analysed, very low dissolved oxygen (3.5 mg/l) and high biochemical oxygen demand (23.7mg/l) were investigated in severely stressed sites.In addition to these, enriched nutrient like phosphorus and nitrate were identified to a level that influences algal growth.According to microbial analysis, total coli forms >180mg/100ml and Escherichia coli type one were isolated.Conclusions: The Canadian Water Quality Index result categorizes the lake as poor state to aquatic life, recreation and drinking.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".