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Record W1988550644 · doi:10.5539/mas.v6n11p49

Groundwater Dynamic and Its Interrelationship with River Water of Bandung Basin Using Environmental Isotopes (18O, 2H, 14C)

2012· article· en· W1988550644 on OpenAlexvenueno aff
Satrio Satrio, S. Paston, Leong Chung Sum, S. Syafalni

Bibliographic record

VenueModern Applied Science · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsnot available
FundersBadan Tenaga Nuklir Nasional
KeywordsGroundwaterGroundwater rechargeEnvironmental isotopesHydrology (agriculture)Surface waterStructural basinGeologyEnvironmental scienceδ18ODrainage basinAquiferStable isotope ratioGeomorphologyGeographyEnvironmental engineering

Abstract

fetched live from OpenAlex

The study of the interrelationship between groundwater and surface water is crucial in groundwater basin research. As an effective tracer in groundwater basin research, environmental isotopes can reveal the interrelationship between river water and groundwater. A research of groundwater and river water alongside river of Bandung area and its surrounding has been carried out. This research was conducted by taking some samples of shallow groundwater, deep groundwater and river water (Citarum, Cikapundung, Cikeruh and Citarik). The objective of this research is to determine groundwater recharge area and to investigate the inter-relationship between groundwater and river water. Based on isotopes ? 2H vs. ? 18O results, there were a mixing process at three location of shallow groundwater with river water. However, the result of isotope 14C does not show interrelationship, either by shallow groundwater or river water. From iso-age contour lines, it could be concluded that the dynamic patterns of deep groundwater show movement derived from the North and the South mountain to the North-West direction. The actual velocity in the area was around 0.25-3 m/year that can be estimated from iso-ages lines contour.

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)0.000
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 score0.315
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.013
GPT teacher head0.195
Teacher spread0.181 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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