Roads & SDGs, tradeoffs and synergies: learning from Brazil’s Amazon in distinguishing frontiers
Bibliographic record
Abstract
Abstract To reduce SDG tradeoffs in infrastructure provision, and to inform searches for SDG synergies, the authors show that roads’ impacts on Brazilian Amazon forests varied significantly across frontiers. Impacts varied predictably with prior development – prior roads and prior deforestation – and, further, in a pattern that suggests a potential synergy for roads between forests and urban growth. For multiple periods of roads investments, the authors estimate forest impacts for high, medium and low prior roads and deforestation. For each setting, census-tract observations are numerous. Results confirm predictions for this kind of frontier of a pattern not consistent with endogeneity, i.e., short-run forest impacts of new roads are: small for relatively high prior development; larger for medium prior development; and small for low prior development (for the latter setting, impacts in such isolated areas could rise over time, depending on interactions with conservation policies). These Amazonian results suggest ‘SDG strategic’ locations for infrastructure, an idea the authors note for other frontiers while highlighting major differences across frontiers and their SDG opportunities.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".