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Record W4200569781 · doi:10.24850/j-tyca-2022-02-07

Estimación de la distribución espacio temporal de la recarga de agua subterránea en regiones húmedas con clima tropical

2021· article· es· W4200569781 on OpenAlexaff
Marcia Lizeth Barrera-de-Calderón, Jaime Gárfias, Richard Martel, Javier Salas-García

Bibliographic record

VenueTecnología y Ciencias del Agua · 2021
Typearticle
Languagees
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsGroundwater rechargeAquiferGroundwaterPrecipitationHydrology (agriculture)Water balanceEnvironmental scienceSpatial variabilityStructural basinGeologyGeographyGeomorphologyMathematics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.255
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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