Advances in Gas Hydrate Research from Energy, Environmental, Engineering And Scientific Perspectives as Informed by Comparative Bibliometric and Scientometric Analyses
Bibliographic record
Abstract
It is estimated that 2 to 20 quadrillion (10^15) cubic meters of methane exist in the Earth’s crust in the form of solid hydrate (also known as methane clathrate). The recovery of this methane, however, presents an enormous challenge to engineers, and it is doubtful if energy needs may not be better satisfied with more sustainable technologies that do not require the level of capital and risk associated with hydrate energy development. Moreover, gas hydrates also participate in the runaway greenhouse effect (RGE), whereby a warming planet leads to a faster rate of hydrate decomposition from permafrost, releasing the potent greenhouse gas at a faster rate than it is removed from the atmosphere, thus cumulatively fueling global warming. These are complicated multistep phenomena, and studies are ongoing to understand the factors that lead to the onset of hydrate decomposition, the rate of methane release, and the reactions that methane undergoes after release, to thus better quantify the impact and seriousness of the RGE. Such research topics on energy and environment, coupled with other research involving hydrates in the field of energy and heat storage and transport among other scientific and utilization themes, illustrate the breadth and diversity of gas hydrate research. To better understand how these topics are advancing relative to each other, to guide effort and investment in the most promising fields and inform policy and regulatory development, bibliometric and scientometric analyses of the scientific literature become a valuable tool. Recent bibliometric and scientometrics analyses on the topic of gas hydrates have been limited to single-topic assessments to gauge the historical development of all related fields of research and delineate the level of cross-institutional and cross-national collaborations. The limitation of these types of analyses is that they exclusively utilize inclusive search strings (e.g., {“Gas Hydrate*” OR “Clathrate Hydrate*” OR “Methane Hydrate*”}), which aims at capturing as much of the relevant literature as possible but does not allow the analysis to differentiate among the various aims and motivations for the research involving the topic. The authors of this presentation have developed a technique for comparative analysis that relies on three main techniques: (i) identifying suitable keywords that represent each unique research direction of the main topic; (ii) using a variety of logical operators to produce independent records of literature on each research sub-theme; (iii) utilizing “publication ratio” and other data normalization techniques to directly compare developments in each sub-theme irrespective of size. The authors originally developed these techniques in a study about climate change versus general climate research published in Heliyon and subsequently applied them in a study about PFAS versus microplastic research published in RSC Advances. Here, the authors demonstrate how these techniques serve to inform about the historical and latest developments in gas hydrate research, with a view to predicting where research is heading and in particular how research is being translated from theory and experiments to industrial and field practice.
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How this classification was reachedexpand
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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.046 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".