Natural Wastewater Processing Systems: Treatment Tanks using plants in Brazil's Izidora basin
Bibliographic record
Abstract
Disorderly urban expansion directly impacts city planning, a phenomenon that induces the settlement of vulnerable populations in areas lacking urban planning.Located in the northern sector of the city of Belo Horizonte (Brazil), the Izidora settlement has become established in the largest preserved fragment of the Atlantic Forest in the city, resulting in deforestation and degradation of riparian areas, affecting the region's watercourses.The Vitória Settlement, located in the Izidora region, is home to 4,500 low-income families.Due to the deficient urban infrastructure and buildings lacking a sewage system, domestic sewage is discharged directly into the watercourses, impacting the quality of aquatic ecosystems.In 2021, the Vitória Settlement was rehabilitated, aiming to restore the water bodies and their riparian forests.Among the recovery interventions, 12 Evapotranspiration Tanks (TEvaps), a nature-based and low-cost wastewater treatment system, were installed.These TEvaps promote the treatment of blackwater using microorganisms capable of decomposing organic matter, combined with plants that facilitate the elimination of water into the atmosphere through transpiration.This study evaluates the results of the TEvaps installed in the settlement through biological, physical, and chemical parameters of water samples collected from the region's watercourses.
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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 | 0.000 | 0.001 |
| 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".