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Record W2790312290 · doi:10.1590/2318-0331.0318170121

Assessment of the potentiometric drawdown in the Guarani Aquifer System in Bauru/SP by a model of analytical elements

2018· article· en· W2790312290 on OpenAlexaff
Vinicius Ferreira Boico, Edson Wendland, José Anderson do Nascimento Batista

Bibliographic record

VenueRevista Brasileira de Recursos Hídricos · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversité Laval
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsDrawdown (hydrology)AquiferGroundwaterHydrology (agriculture)Environmental scienceGroundwater flowOverexploitationGeologyWater resource managementEcologyGeotechnical engineering

Abstract

fetched live from OpenAlex

ABSTRACT The recent drought events and the population growth in São Paulo State (Brazil) have caused many municipalities to increase the groundwater exploitation of the Guarani Aquifer System (GAS) for the public water supply. In Bauru City/SP, the extraction of water from this Aquifer is expected to increase from 3699 m3/h (2014) to 4465 m3/h (2034). However, its long-term overexploitation may compromise the amount of available groundwater. The Analytic Element Method was used for groundwater flow modeling at steady-state, which includes the regional flow, the water withdrawal from wells and the main local geological conditions. The hydraulic gradient 0.82 m/km was estimated in the GAS in Bauru City. The potentiometric drawdown in the GAS in Bauru was estimated as 50 m since the beginning of the groundwater exploitation. The drawdown for the groundwater pumping scenario in 2014 is higher than 15 m in Piratininga and Agudos and lower than 10 m in further cities. The expected drawdown for the scenario of 2034 can reach 15 m in comparison with the scenario of 2014, in the North of Bauru City, where the future wells will be located.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.261
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2018
Admission routes1
Has abstractyes

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