Environmental Management Strategy With a Focus on Sanitary Sewage and Urban Development
Bibliographic record
Abstract
The Amazon boasts a great environmental heritage and abundance of water resources, it is in this remarkable biome where the worst rates of access to basic sanitation services and public health indicators are. This research aims to use the SWOT analysis to generate knowledge capable of subsidizing the public manager in the elaboration of strategic planning to implement sanitary sewage according to the National Policy for Basic Sanitation in a municipality of the Brazilian Amazon under development and urban expansion. The practices adopted for this task relate to the concepts of the SWOT matrix, public policies for basic sanitation with a focus on sanitation, urban sprawl and social impacts on human health related to poor sanitation. Identifying in the municipal legal norms the strategic planning to order the development and the urban expansion and the practices foreseen to meet the National Policy for the Basic Sanitation and implantation of the sanitary sewage in the studied municipality. This is a descriptive exploratory research based on document and field research according to legal and environmental norms with qualitative and quantitative results. Despite the result demonstrating that the strategic planning for the implementation of sewage in urban expansion projects results in sustainable urban development, the municipality under study did not meet the legal and environmental standards for the implementation of sewage in new subdivisions causing environmental, social and economic problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".