Identificação de forçantes ambientais atuantes no aumento de vulnerabilidade em comunidades da Bacia Amazônica
Bibliographic record
Abstract
A pandemia de COVID-19 acentuou vulnerabilidades em comunidades tradicionais na América Latina. Junto com os desafios que a própria doença instaurou, essas comunidades estão expostas à múltiplas ameaças socioeconômicas e ambientais, que se cruzam e moldam os caminhos de recuperação traçado por cada uma. O projeto Vozes em Recuperação se concentra em compreender e apoiar os caminhos de recuperação de comunidades marginalizadas no Brasil, Colômbia e Peru. Este trabalho é uma primeira iniciativa para identificar ameaças ambientais que acometem cada sítio de estudo escolhido. Foram identificadas múltiplas ameaças ambientais, como mudanças climáticas e incêndios florestais. O processo de tomada de decisão em torno da recuperação sustentável dessas comunidades pode ser mais eficaz, uma vez que essas ameaças e vulnerabilidades sejam diagnosticadas e melhor compreendidas. ABSTRACT: The COVID-19 pandemic has accentuated the vulnerabilities of traditional communities in Latin America. Along with the challenges that the disease itself has created, these communities are exposed to multiple socioeconomic and environmental threats, which intersect and shape the recovery paths traced by each one. The Voices in Recovery project focuses on understanding and supporting the recovery paths of marginalized communities in Brazil, Colombia, and Peru. This work is the first initiative to identify environmental threats that affect each chosen study site. Multiple environmental threats were identified, such as climate change and forest fires. The decision-making process around the sustainable recovery of these communities can be more effective, once these threats and vulnerabilities are diagnosed and better understood.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".