Challenges in the context of single-use plastics and bioplastics in Brazil: A legislative review
Bibliographic record
Abstract
Plastic has been present in our lives for the past century as an essential material for many commodity items. However, the same properties that make plastic convenient are also responsible for the current dramatic environmental pollution. As an alternative, most of the world has been working with technological innovations, and one of its strategies is the use of bioplastics. Despite being considered environmentally beneficial by some people, there are still developments and discussions that need to be made. This article aims to present a legislative review and discusses the difficulty in implementing policies related to the incentive of the bioplastics market, as well as presenting some state and municipal laws, already prohibiting single-use plastics in Brazil. These laws aim to encourage the substitution of these plastics for biodegradable ones. However, it still has gaps and a lack of clarification on how the banning of disposable plastics and their substitution will be beneficial since composting is still an incipient process in the country. It is also the purpose of this article to discuss the challenges in the context of the Circular Economy, as well as the potential solution based on the creation of public policies aimed at improving waste management, in addition to clearer legislation on alternatives to single-use plastics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".