Mathematical Modeling of Groundwater Surface Water Interaction Represented using Boussinesq Equation – A Bibliometric Study
Bibliographic record
Abstract
Water is a significant, a must-needed natural resource for mankind and all living species on earth. Apart from for drinking and domestic needs, water is being used for other purposes like farming and industry. Groundwater is a natural, easily available water source. It is predicted that, by 2025, two-thirds of the world's population may face water shortage. Due to anthropogenic activities, quality of groundwater is hampered. The study of groundwater with the help of mathematical modeling gives a thorough idea of all the parameters which affect groundwater. Bibliometric studies aids researchers and funding agencies to focus on the research area in which more attention is required. It helps the new researchers to identify the varied areas pertaining to the research field in which one needs to focus more to get fruitful results. The use of Boussinesq equations is one of the leading methods in modeling the problem in groundwater analysis. This paper provides an overall picture of research carried out in groundwater analysis in the current century. This analysis is based on the publications available in Scopus database using some graphical tools. Figures and facts are interpreted in the form of plots, charts, and tables. This survey revealed that the maximum publications are from journals and conferences and USA lead the publications in this area. A lot of publications in this area are from journals of Fluid mechanics followed by journals of Civil engineering. The number of papers and papers published in journals of different areas shows the importance of this research topic and also the thrust.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".