Estimación de la distribución espacio temporal de la recarga de agua subterránea en regiones húmedas con clima tropical
Bibliographic record
Abstract
The precise determination of groundwater recharge variation is a fundamental task for the sustainable planning of groundwater resources, particularly in heavily pressured aquifers. In order to determine the spatial and temporal variability of groundwater recharge in an urban aquifer with a humid climate, such as the San Salvador aquifer, two Mass Balance methods were used (one in the subsurface zone, Soil Water Balance (SWB) and another in the saturated zone, Chloride Mass Balance (CMB)). The SWB was calculated on a daily scale for four years (2012-2015) through the modified Thornthwaite and Mather method, using a set of daily climatic data grids and physical data from the study area. The CMB was used to determine the groundwater recharge in drilled wells and springs samples taken during 2009 and 2016, in the upper part of the basin, where various studies suggest that the main aquifer recharge occurs. The results of the SWB indicate a strong temporal and spatial variation of the recharge in the study area, which can vary between 326 and 561 mm year-1, in dry and wet years, respectively. The CMB results showed consistency with the SWB, groundwater recharge values ranged between 313 and 693 mm year-1. In both methods the mean annual recharge is similar and represents between 20% and 30% of the precipitation. The application of both methods could be used in similar areas, the selection of the method will depend on the objectives of the study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".