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Record W7001202235

Imaging the Arctic Seafloor to Examine Escape Routes of the Greenhouse Gas Methane

2018· article· en· W7001202235 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of New Hampshire Scholars Repository (University of New Hampshire at Manchester) · 2018
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsArcticArctic geoengineeringSeafloor spreadingCryosphereMethanePermafrostArctic dipole anomalyArctic ice packGreenhouse gasDeglaciation
DOInot available

Abstract

fetched live from OpenAlex

Vast amounts of the greenhouse gas methane are stored under the ocean floor as ice-like, crystalline compounds called hydrates. These naturally occurring structures form under high pressure and in low temperatures, when water molecules encage and stabilize gases. These hydrocarbon gases originate from thermogenic/abiogenic sources deep below the ocean floor and/or from biogenic shallow sources. The Arctic holds vast undiscovered reserves of hydrates with mixtures of thermogenic and biogenic methane. The last deglaciation some 16,000 years ago and today’s ocean warming cause them to melt and release methane from—for example— fault networks and craters of the ocean floor. The most dominant forces to have ever affected large regions of the Arctic are the growth and collapse of the ice sheets in the northern hemisphere and the recent climate change. Obviously, we cannot observe firsthand the natural world of the prehistoric past. However, we do have high-resolution 3D/4D seismic, as well as seafloor, observations, and well-constrained numerical modelling. Using this technology, we have been able to see into sub-seabed fluid migration and seabed-fluid expulsion systems. Deep hydrocarbon reservoirs exist today beneath the pressures of the ice sheets in Greenland and Antarctica. Signs of instability and fluid migration may become commonplace under scenarios of fast ice retreat. Presenter Bio Jurgen Mienert is a Professor for Applied Geophysics and Arctic Marine Geology at UiT—The Arctic University of Norway, Tromsø since 1998. Jürgen chaired the European Ocean Margin Research Consortium (OMARC) from 2003–2006 with a focus to better understand the response of European margin systems to climate change. He led the Department of Geology from 2008–2012, before his team received an award for building a Centre of Excellence by the Norwegian Research Council in 2012. He was Director of this Centre for Arctic Gas Hydrate, Environment and Climate (CAGE) from 2013-2017. His research focuses on understanding the rate at which rising ocean temperatures can destabilize shallow, Arctic methane hydrate reservoirs leading to geohazards and methane release to the ocean and atmosphere. Jürgen received his Ph.D. in geoscience from the Christian-Albrechts University (CAU) at Kiel, Germany (1985) before he was awarded a post-doctoral position at Woods Hole Oceanographic Institution, USA (1985–1988) and returned to Kiel in 1988 to join the newly established Centre for Marine Geosciences—GEOMAR—at the CAU, Kiel. His collaboration with teams from Russia, USA, Canada, and Europe on polar shelf and slope environments facilitated active cooperation among hydrocarbon companies, technology providers, and Arctic research groups. His many sea expeditions included contributions to the Norwegian deep-water gas field “Ormen Lange” investigation, dives with Russian submersibles MIR from RV Keldysh (1998) to the deep ocean floor at the giant submarine “Storegga slide” on the Mid-Norwegian Margin and to natural gas release sites along the Barents-Svalbard margins, which provided an indispensable guide for methane hydrate studies. Jürgen has authored and co-authored more than 200 papers in peer-reviewed journals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.018
GPT teacher head0.216
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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