Assessment of the water and sediments quality around the coastal submarine sewage outfall in Guarujá, São Paulo, Brazil
Bibliographic record
Abstract
The discharge of domestic sewage is one of the most common types of marine pollution, namely through submarine outfalls. In this study, water and sediments of the coastal submarine sewage outfall in Guarujá, São Paulo, Brazil were assessed during the high (January) and low (April) tourist seasons in 2018. The Canadian Council of Ministers of the Environmental Water Quality Index (CCMEWQI) showed a “marginal” water quality, in both seasons, where dissolved oxygen, biochemical oxygen demand, oil and greases, ammonia, surfactants, aluminium, lead, copper, nickel, Escherichia coli and Enterococci showed potential ecological risks. However, no mutagenic potential was detected in the complex mixture (Ames Salmonella/microsome test: MI<2), and no protozoa and Salmonella bacteria were found. In the sediment, a total of 25 benthic taxa were inventoried, suggesting that the macrofauna is not under contamination stress. Cadmium, lead, copper, chromium, nickel and zinc were below the Threshold Effect Level, and the Geoaccumulation Index was <0. Furthermore, the absence of acute toxicity to the test organism Kalliapseudes schubartii (EC50: 96h) and the Shannon-Wiener diversity index (H’: 2.5 to 3.5 bits/ind) suggests healthy or unpolluted environments. However, the deviation of some environmental indicators suggests the need of continuous monitoring based on field measurements.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".