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 isolated 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. This study aimed to quantify the amount of water that can be retained in a natural wetland and to propose a model of groundwater recharge. We used remote sensing techniques involving unmanned aerial vehicle to map the wetland and highlight its internal morphology, using a red–green–blue orthomosaic and a digital surface model. The 2-D inversion and a pseudo-3-D model from electrical resistivity tomography data were used to visualize the subsurface structures and hydrologic flow paths. The wetland is a reservoir storing up to 416.996 m3of water during the rainy months. Distinct internal compartments characterize the wetland topography and different water-volume storage, lower in the border and higher in the center. A leakage point connects surface water to groundwater through direct vertical flow, which constitutes the aquifer recharge zone. Remotely sensed very high-resolution images allied with geophysical techniques allowed complete surface and subsurface imaging and offered visual tools that contributed to understanding the hydrodynamics of the wetland.
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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".