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Record W6913030144 · doi:10.5281/zenodo.8003645

Advances in Gas Hydrate Research from Energy, Environmental, Engineering And Scientific Perspectives as Informed by Comparative Bibliometric and Scientometric Analyses

2023· article· en· W6913030144 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsClathrate hydrateMethaneGreenhouse gasSeriousnessHydrateGlobal warmingPermafrostNatural gasBiosphere

Abstract

fetched live from OpenAlex

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.

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.

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 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
Observationallow
models agreeAgreement 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0110.046
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.061
GPT teacher head0.317
Teacher spread0.256 · 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

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicMethane Hydrates and Related PhenomenaCategoryBibliometricsFrench-language works237,207