Brazilian National Solid Waste Policy Applied As a Tool to Enhance an University Campus Waste Management
Bibliographic record
Abstract
The aim of this work is to discuss the Brazilian National Solid Waste Policy potential to enhance Brazilian universities waste management by analyzing the University of São Paulo, Campus of São context in accordance with the national policy requirements. Universities in Brazil lack a legal instrument to strengthen its waste management, which brings this paper innovation by applying the Brazilian waste policy as a standardization instrument to adapt waste management at Brazilian higher education institutions. The research used a descriptive and qualitative approach, data were collected from literature review, university documents and semi structured interviews, a case study approach is used to analyze the campus solid waste management activities, procedures and operations inherent the. The main findings conclude that University of São Paulo waste policy is being deployed, based on Brazilian Solid Waste National Policy requirements, confirming it translation into a potential framework tool to support decision making for adequacy of environmentally sound management of Brazilian. Further studies are required ex-post the policy enactment to assess the impacts of the waste policy at the university impacts.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".