Citizen Science for Environmental Monitoring in the Eastern Region of Bolivia
Bibliographic record
Abstract
The eastern region of Bolivia is of high conservation interest due to the presence of the Chiquitano Dry Forest, Dry Chaco, Pantanal and Cerrado ecoregions. However, this region is under high pressure from various anthropogenic threats, which requires continuous monitoring. An alternative for this monitoring is the use of mobile applications designed under the concept of citizen science, in which local stakeholders are part of the process of obtaining information and finding solutions to environmental problems in their territories. The main objective of this study was to evaluate the information obtained during environmental monitoring with a citizen science approach in the eastern region of Bolivia. We developed a public electronic form for the ArcGIS Survey123 mobile application to capture spatial data of nine thematic variables. Between 2021 and 2023, we conducted 16 training courses in 12 population centers, with attendees from 98 communities in 6 municipalities in the region. A total of 360 volunteers from different sectors participated in the training, including technicians from public and private institutions, park rangers, community representatives and citizens. We obtained a total of 379 records, of which 70.4% were recorded near communities and the rest within protected areas. The results were reclassified and grouped into three clusters: human activities, water resources and biodiversity. In the human activities cluster, the categories with the highest number of records were wildfires and deforestation. In the water resources cluster, the categories with the most records were cattle waterholes and streams, but one of the most notable records was the reduction of wetlands in a sector of the Bolivian Pantanal. In the biodiversity cluster, the main reports were for mammals, and among the most notable records obtained were the footprints of the jaguar (Panthera onca). This monitoring tool made it possible to generate and use high-quality information in different sites in the eastern region in almost real time, which could help strengthen the interactions and relationship with users in environmental dialogue and governance processes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".