Climate Change and Ecosystem Services: A Participatory Approach in a Brazilian Mountainous Region
Bibliographic record
Abstract
Climate change is present in all sectors of global societies, causing various damages, including to agriculture. In this process, understanding the perception of the most affected population and including them in decision-making is a matter of social justice and a more assertive path. Aiming to obtain subsidies for better adaptation to climate change in family farming, this study established a methodology for evaluating the risks of climate change impacts on ecosystem services, related to water and food security, under the perception of rural actors in the mountainous region of the Rio de Janeiro state, Brazil. The methodology consisted of five steps, including the application of the questionnaire to 29 rural actors and ending with validation of the results in a focus group. The main results obtained were that the interviewees perceive the risks of the impacts of climate change in ES, both related to water and food security. Additionally, 24.14% of interviewees mentioned that their family has a monthly income below the Brazilian minimum wage, which exposes their food insecurity. In relation to water security, the risks classified as Very High were mainly in relation to low per capita investments in adaptation policies and infrastructure for environmental protection, between others. The use of many chemical inputs in agriculture also highlighted as a risk to water and food security. Regarding the methodology, it concluded that it was effective in obtaining the perception of the rural actors interviewed regarding the impacts of climate change on ecosystem services at a local scale.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".