Gastrointestinal Impact of Flatulence-Causing Compounds in Foods: A Scientometric Study
Bibliographic record
Abstract
Flatulence, or the passing of gas, can be caused by various factors, including the consumption of foods that contain hydrogen sulfide. When these foods are digested, they can release hydrogen sulfide gas, accumulating in the intestines and releasing flatulence. The present study aimed to give a scientometric overview of publications on flatulence regarding productive sources/journals, top active authors, foremost affiliations, prominent countries, and most used author’s keywords. In all, 4752 articles were downloaded from the Web of Science [WoS] database [2007-2021], consisting of journal articles, review articles, and the English language. Data analysis and network visualization maps were created using Micro Soft Excel, Biblioshiny, and VOS viewer. The results indicate that the most productive author was Germain DP contributed 53 papers with the highest received 4026 citations. The topmost journal contributions were Plos One with number 113. Findings revealed that the top two institutions were from Canada. It was revealed that the United States, and China were the second-ranked in document contributions related to flatulence research. Articles produced by single-country publications had a higher number of papers compared with papers produced by multiple-country publications. Clinical research is a crucial aspect of healthcare as it helps determine the safety and effectiveness of new medical interventions, which can be used to improve patient outcomes related to flatulence research.
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.101 | 0.143 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".