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Record W2060717241 · doi:10.4043/12114-ms

Variation in Methane Hydrate Structure and Composition

2000· article· en· W2060717241 on OpenAlexaboutno aff
Richard B. Coffin, K. S. Grabowski, Jennifer Linton, V. Thieu, Yuval Halpern, P. A. Montano, R.D. Doctor

Bibliographic record

VenueOffshore Technology Conference · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsMethaneClathrate hydrateCarbon fibersPermafrostCarbon cycleEnvironmental scienceHydrateEarth scienceGeologyOceanographyEnvironmental chemistryChemistryMaterials scienceEcologyEcosystem

Abstract

fetched live from OpenAlex

Abstract Methane hydrates are now recognized to be present in substantial quantities along most ocean margins. Current estimates indicate that methane in hydrates exceeds fixed carbon in petroleum reserves by about a factor of three. This is a large potential source of energy, capable of lasting for centuries. In response, Japan, Korea, Norway, India and Canada are actively investigating the acquisition of methane from hydrates as an energy source. Their success could significantly restructure the global economy. While a large international focus is on methane hydrates as a source of energy, hydrates are also thought to influence ocean carbon cycling, global warming, and coastal sediment stability. Thus, methane hydrates are a significant emerging research issue. While the global distribution and quality of hydrate fields are certainly being pursued, this presentation describes more fundamental investigations of naturally formed methane hydrates. Specifically, the Naval Research Laboratory (NRL) has initiated research on the influence of hydrates on ocean floor geoacoustical and geotechnical properties. In addition, NRL and Argonne National Laboratory (ANL) have studied methane hydrates from different regions of the world ocean for variations in structure and composition. This presentation compares the methane hydrate structure, composition, and source of carbon for samples taken from the Norwegian-Greenland Sea and the Gulf of Mexico. Structural analysis is compared using x-ray diffraction. Composition is described from gas chromatography, and the carbon source is identified with carbon isotope analysis. Introduction Methane hydrates vastly exceeds other carbon reservoirs in the ocean1. Research demonstrates that both thermogenic and biological carbon sources contribute to the formation of the methane hydrates2,3. There is a large variation in the sources of methane between ecosystems. The carbon cycling that controls methane production in the ocean floor is related to a complex mixture of biological, chemical and physical processes. Physical factors on the ocean floor relate to motion of the continental plates resulting in expulsion of fluids and volatile compounds, flows of geothermal energy toward the sediment-ocean interface, cold pressure mediated seeps of reduced compounds, and high levels of land based carbon transport to the ocean floor. These processes result in a variety of chemical speciation through key elemental pools that enhance microbial activity. For example, ocean floor thermal seeps have a high flux of reduced compounds, e.g. CH4, H2S and NH4, that supports the chemoautotrophic population. These cycles serve as the base of the food chain where enzymatic oxidation results in fixation of CO2 into cellular biomass. In active regions this results in CO2 serving as the terminal electron acceptor and methane production and fixation in hydrates. The variation in thermogenic and biological methane sources influences the hydrate content and structure. The degree that these factors contribute to the hydrate stability and formation warrants further research. Results from this research topic will contribute to methane hydrate mining strategies, understanding of coastal stability and environmental health, analysis of ocean carbon cycling, and predication of global warming.

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

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
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

Citations2
Published2000
Admission routes1
Has abstractyes

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