Análisis de la deforestación de la Amazonia peruana: Madre de Dios
Bibliographic record
Abstract
Este artículo tuvo por objetivo sistematizar las evidencias de la deforestación y determinar los principales factores de pérdida de bosques en el en el departamento de Madre de Dios, Perú. Se realizó una búsqueda de investigaciones científicas relacionadas a “deforestación”, “deforestación amazonia peruana”, deforestación Madre de Dios”. Se analizaron artículos científicos publicados en base de datos de revistas indizadas. Se optó por un diseño de estudio no experimental, descriptivo. Para la recolección de datos se aplicó la técnica de análisis de documentos. A partir de las evidencias se concluye que, en la Amazonía sur, principalmente en Madre de Dios, se concentran los puntos de mayor desbosque. Además, los principales factores de pérdida de los bosques son la minería ilegal y actividades agropecuarias en su mayoría ilegales, dentro de estas dos actividades la minería ilegal es la causante de mayor porcentaje. También prevalecen los aspectos negativos como la pérdida de biodiversidad, contribuyéndose al cambio climático.
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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".