Climatic and Anthropic Influence on the Geodiversity of the Maranhão Amazon Floodplain
Bibliographic record
Abstract
The Maranhense Amazon floodplain shelters a Ramsar site established by the United Nations for the protection of wetland biodiversity. Despite its protected ecological status, the impacts from deforestation, burning, the agricultural and livestock industries, are on the rise. Knowledge of the spatial distribution and temporal dynamics of these impacts are important to improve the understanding of how this region is affected. Data on increasing deforestation and hot pixels were used to evaluate the anthropogenic pressure under the geodiversity of the region, relating them to the environmental variables (rainfall, Normalized Difference Vegetation Index and Deforestation annual deforestation rate) measured through the rainfall data and the Normalized Difference Vegetation Index (NDVI). In this study, the potential of remote sensing and geographic information system. The time series were used from 2001 to 2016 for all variables. We observed a strong negative and significant correlation between hot pixels and NDVI, while hot pixels increase, the vegetation indexes tend to decrease. In 2006 an abrupt fall in the NDVI occurred due to the marked increase in the deforested area. In 2010, the NDVI reached its highest levels, because the vegetation responded to the highest rainfall observed in the period in 2009. Unit 4 presented the highest pixels number in the period evaluated (2,978 pixels; 55% of the total). There is a significant correlation between NDVI and rainfall.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".