A study of contaminated land in São Paulo city, Brazil and mainly adopted remediation process face a deficient database
Bibliographic record
Abstract
Since emblematic environment contaminated cases, as Love Canal in United States was found out, the discussion regarding contaminated land are common in scientific community, covering subjects as urban planning, public health and availability of natural resources.Concerns with environment contamination are also relevance because can impact the progress to The Sustainable Development Goals, a global blueprint to develop a sustainable future.Contaminated land refers to areas that have been contaminated by industrial activities, irregular waste disposal or toxic substances.Lack of management of these areas can harm the development of sustainable future, for the cities and citizens.Therefore, the existence and availability of data on the areas that are contaminated is necessary to create better urban planning.In Brazil there are not federal programs to deal with contaminated sites and a federal database regarding this information is absent.However, São Paulo State has been a pioneer in management of contaminated areas in Brazil, developing laws and regulations, since 1999.The aim of this research is to present data regarding contaminated areas in municipality of São Paulo, in five districts, providing information about the scattering of contaminated areas across the districts, the main polluting activity, also observing aspects as revitalization and clean-up process to realize if the remediation process is occurring in the city.This study is a qualitative exploratory research, with information obtained from secondary sources.The results indicated that the main polluting activity is gas station, the process of revitalization and clean-up is happening in all districts evaluated, also showed that environment compartment more affected is Groundwater.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".