Bibliographic record
Abstract
Abstract Gas hydrates, or clathrate hydrates, is a multidisciplinary field of research involving potential applications to the oil and gas industry, energy security, and innovative technological applications with literature referenced back to 1810. The field of gas hydrates or clathrate hydrates has progressed over the past several decades from academic curiosity‐driven research to industrially relevant research related to flow assurance and methane hydrates in nature as an energy resource. In the recent few decades, several innovative and sustainable applications have emerged with gas hydrate or clathrate hydrate as a technology enabler. In this work, I present a bibliometric analysis of the field of gas hydrates or clathrate hydrates for the period from 1901 to 2020 from the Web of Science core collection database of Clarivate Analytics. In total, 12 152 journal publications (review and original research articles) were analyzed from Web of Science core collection database spanning 121 years (1901–2020). Top countries, top cited review articles, and original research articles along with top source titles (journals) are identified and highlighted. In addition, the field classifications and citation rate trends have been analyzed and presented. Network visualization maps are presented for countries, sources, and organizations by analysing citations in VOSviewer. Co‐occurrence analysis is performed to identify the top keywords and their links through network visualization based on VOSviewer.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.125 | 0.213 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".