Measuring the Impact of Brazilian Transport Systems on the 2030 Agenda Goals
Bibliographic record
Abstract
This paper aims to propose indicators to measure Brazilian transport systems' impact on meeting the 2030 Agenda and to analyze advances of Brazilian transport systems in terms of sustainable development over the last decade. The proposed indicators were based on a literature review and data availability. Time series data (2010-2019) were obtained to analyze the situation of Brazil. From 27 proposed indicators, only 12 showed some evolution based on the before-after method, relating to improvements in cleaner transport modes, such as railways and waterways, in exclusive lanes for public transport, and in improving active transport infrastructure. This scenario presents Brazil's challenges over the next ten years to achieve the Sustainable Development Goals proposed by the 2030 Agenda. Results reinforce the importance of the transport sector to contribute to the world's sustainable development. Therefore, this paper contributes to improving the analysis hereafter of this thematic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".