Land use change analysis of the flooded area in the Guaiba Hydrographic Region in southern Brazil 2024
Bibliographic record
Abstract
Abstract. The impact of extreme weather events is largely influenced by land cover, as demonstrated by the catastrophic flood in Rio Grande do Sul (RS), Brazil, in May 2024. Over 200mm of rain fell daily in numerous municipalities, displacing 2.3 million people. Although forests cannot entirely mitigate such extreme rainfall, they can help reduce runoff and related damages. We conducted a geospatial analysis to assess land use changes from 1985 to 2022 in the Guaíba Hydrographic Region, applying a three-step raster analysis using GIS tools. We classified pixels as natural or anthropogenic to monitor vegetation changes over four periods. Data processing efficiency improved significantly with a PostgreSQL approach, reducing query time from 20 hours to five minutes after a lenghthy initial pre-processing. Our findings indicated a higher long-term anthropogenic influence in flooded areas, with vegetation loss in Pampa Grasslands (PP) at 33.2%, compared to 18.1% for the Atlantic Forest (AF) and 16.8% in flooded areas. Between 1985 and 2022, we observed a fluctuating conversion rate of natural forests, with an overall loss in grasslands at an increasing annual rate. Soybean cover rose dramatically during these years, growing 430% until 2022, diminishing natural pastures in the Pampa biome. Our analysis emphasizes the effectiveness of forest protection policies while revealing that grassland areas remain poorly managed despite their crucial role in mitigating flood impacts.
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 | 0.001 | 0.002 |
| 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".