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Record W4405435813 · doi:10.26599/jgse.2024.9280031

Development, hotspots and trend directions of groundwater numerical simulation: A bibliometric and visualization analysis

2024· article· en· W4405435813 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Groundwater Science and Engineering · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsVisualizationGroundwaterEnvironmental scienceComputer scienceGeographyGeologyData miningGeotechnical engineering

Abstract

fetched live from OpenAlex

Groundwater is a vital component of the hydrological cycle and essential for the sustainable development of ecosystems. Numerical simulation methods are key tools for addressing scientific challenges in groundwater research. This study uses bibliometric visualization analysis to examine the progress and trends in groundwater numerical simulation methods. By analyzing literature indexed in the Web of Science database from January 1990 to February 2023, and employing tools such as Citespace and VOSviewer, we assessed publication volume, research institutions and their collaborations, prolific scholars, keyword clustering, and emerging trends. The findings indicate an overall upward trend in both the number of publications and citations concerning groundwater numerical simulations. Since 2010, the number of publications has tripled compared to the total before 2010, underscoring the increasing significance and potential of numerical simulation methods in groundwater science. China, in particular, has shown remarkable growth in this field over the past decade, surpassing the United States, Canada, and Germany. This progress is closely linked to strong national support and active participation from research institutions, especially the contributions from teams at Hohai University, China University of Geosciences, and the University of Science and Technology of China. Collaboration between research teams is primarily seen between China and the United States, with less noticeable cooperation among other countries, resulting in a diverse and dispersed development pattern. Keyword analysis highlights that international research hotspots include groundwater recharge, karst water, geothermal water migration, seawater intrusion, variable density flow, contaminant and solute transport, pollution remediation, and land subsidence. Looking ahead, groundwater numerical simulations are expected to play a more prominent role in areas such as climate change, surface water-groundwater interactions, the impact of groundwater nitrates on the environment and health, submarine groundwater discharge, ecological water use, groundwater management, and risk prevention.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Scholarly communication
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0250.045
Science and technology studies0.0000.000
Scholarly communication0.0020.001
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 head0.071
GPT teacher head0.366
Teacher spread0.295 · 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