Natural superficial water storage and aquifer recharge assessment in Brazilian savanna wetland using unmanned aerial vehicle and geophysical survey
Bibliographic record
Abstract
Human pressure on the water resources provided by natural wetlands has intensified in Brazil due to an increase in agricultural land equipped with irrigation. However, the amount of water stored in these areas, and its contribution to aquifer recharge is unknown. The objectives of this study were to determine the amount of water that can be retained in a natural wetland surrounded by farmland and to propose a model of groundwater recharge. We used remote sensing techniques involving Unmanned Aerial Vehicle (UAV) to map the area and highlight the wetland internal morphology, using RGB orthomosaic and its respective digital surface model. According to the topography of the study area three compartments were defined. Compartment 1, corresponding to the external border of the wetland, can store 313,121.00 m³ of water; compartment 2 and 3 can store 85,923.20 m³ and 17,952.10 m³, respectively. The 2D inversion and a pseudo-3D model produced from Electrical Resistivity Tomography (ETR) data were used to visualize the subsurface geologic structure and hydrologic flow paths. Our results showed the direct interactions between groundwater (GW) and surface water (SW) in the center of the wetland (compartment 3), which constitutes the aquifer recharge zone with vertical infiltration. As the depth increases, infiltration and water flow deviate laterally in the southwest and northeast direction. The wetland is characterized as an unconfined aquifer that plays an important role in the hydrogeological dynamics of the catchment. Remotely sensed images allied with geophysical techniques allow complete surface and subsurface imaging and offers visual tools that contribute to understanding the hydrodynamic of an area.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".