Bibliographic record
Abstract
In the last 21 years, Bolivia has recorded a series of thousands of wildfires that impacted an area of 24 million hectares, mainly in the departments of Beni and Santa Cruz. In this sense, identifying trends in the increase of natural vegetation after wildfires is a fundamental step in implementing strategies and public policies to ensure ecosystem recovery. The main objective of this study was to evaluate the spatial trends of the increase and decrease in vegetation affected by wildfires for the whole of Bolivia, for the period 2001–2021, using non-parametric tests, through the analysis of Normalized Difference Vegetation Index (NDVI) remote sensing products. The results indicated that 53.6% of the area showed an increasing trend (p < 0.05) and 15.9% of the area showed a decreasing trend (p < 0.05). In terms of land cover type, forests were proportionally represented by 18.1% of the areas that showed an increasing trend (p < 0.05) and 3.0% of the forests showed a decreasing trend (p < 0.05). In contrast, non-forested areas showed an increasing trend of 35.5% and 12.9% showed a decreasing trend (p < 0.05). It can be concluded that there is a continuous regeneration process throughout the country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".