Water quality index in an Urban Watershed
Bibliographic record
Abstract
This study aimed to verify the water quality of Ribeirão das Pedras (Stones River), Campinas, São Paulo, Brazil through the implementation of the Water Quality Index (WQI) and comparison with Brazilian legislation (Resolution of the National Environment Council -CONAMA 357/2005), thus being able to initiate discussions about anthropic interferences in watercourses located in urban areas.Ribeirão das Pedras is part of an urban watershed that suffered, and still suffers, from the rapid and intense urban and housing boom, finding its territorial space almost fully occupied.For the execution of this work, six sample points were defined in order to allow a discussion between the land use within their respective drainage area and the results of the WQI applications.The WQI is composed of nine parameters: dissolved oxygen, biochemical oxygen demand, nitrogen, temperature, thermotolerant coliforms, turbidity, phosphorus, pH, and total solids.The first sample point refers to the main watercourse source, four sample points are located throughout the watershed and the last point is located in its base level, at the confluence between Ribeirão das Pedras and its main stem, Ribeirão das Anhumas (Anhumas River).The results of water quality analysis obtained based on the WQI concept were featured as 'GOOD'; however, the isolated analysis of each parameter allows to compare them with the Brazilian legislation, where it appears that none of the points meets all established quality parameters.Thus, it can be concluded that the watercourse suffers significant impacts along its course, probably derived from the use of the surrounding drainage areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".