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Record W1985385412 · doi:10.4043/18011-ms

Scientific Ocean Drilling: Characterizing and Sampling Methane Hydrates

2006· article· en· W1985385412 on OpenAlexaffabout
Frank R. Rack, Peter Schultheiss, David Goldberg, M.A. Storms, D. Schroeder, B. Julson, Mitchell J Malone, Timothy S. Collett, Michael Riedel, P. E. Long

Bibliographic record

VenueOffshore Technology Conference · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsMethaneDrillingSampling (signal processing)Petroleum engineeringGeologyScientific drillingClathrate hydrateOceanographyComputer scienceEnvironmental scienceEarth scienceHydrateChemistryEngineeringMechanical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract Scientific ocean drilling programs such as the Deep Sea Drilling Project (DSDP, 1968-1983) and Ocean Drilling Program (ODP, 1985-2003) have pioneered the study of marine methane hydrates through the development and application of sampling tools, wireline measurements and other techniques used to characterize hydrate deposits along continental margins. These tools and techniques have evolved over many years of engineering development and field trials on multiple expeditions, which have been marked by strong collaborative efforts among academic and industry participants with support from government sponsors. This paper will describe the current status of some of these tools and measurement systems and discuss their potential use in global deepwater exploration of marine methane hydrate. Knowledge of the gas concentration in deep sediment is critical for understanding the dynamics of hydrate formation and the effect hydrates may have on the physical properties of the sediment. However, reliable data on gas concentration are difficult to obtain. The only way to determine true in situ concentrations of natural gas in the sub-seafloor is to retrieve cores in an autoclave chamber that maintains as closely as possible in situ conditions. Characterizing natural marine methane hydrate deposits helps to advance our understanding of these ubiquitous deposits and determine their role as a hazard to be avoided or a potential resource to be explored. Introduction To date, three dedicated scientific ocean drilling expeditions have been undertaken to advance our understanding of marine methane hydrates, namely, ODP Legs 164 (Blake Ridge and Carolina Rise; Ref. 1) and 204 (Hydrate Ridge, offshore Oregon; Ref. 2) and Integrated Ocean Drilling Program (IODP) Expedition 311 (Cascadia Margin, offshore Vancouver Island, Canada; Ref. 3). These dedicated expeditions, which served to sequentially advance the tools, methods and procedures used to study hydrate deposits, were highly successful because of strong collaborative efforts among engineers, scientists and technicians to develop, test and deploy new technologies in innovative ways. For example, closely spaced measurements of temperature made using thermistors inserted into sediment through plastic core liners during ODP Leg 164, evolved into continuous noninvasive measurements of thermal anomalies made using infrared thermal imaging cameras during ODP Leg 204. Similarly, the availability of a single tool for wireline pressure coring on ODP Leg 164 (Ref. 4), evolved into the use of multiple pressure coring tools on ODP Leg 204 due to the synergistic efforts of U.S., European and Japanese groups of engineers and scientists focused on similar research and development goals over several years (Ref. 5). These tools and techniques have coalesced into a set of integrated operational procedures that serve to provide a robust system for characterizing methane hydrates in their natural environment and in the laboratory onboard the JOIDES Resolution.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.227
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2006
Admission routes2
Has abstractyes

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