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Record W2736231651 · doi:10.1038/s41598-017-05783-2

Strong geologic methane emissions from discontinuous terrestrial permafrost in the Mackenzie Delta, Canada

2017· article· en· W2736231651 on OpenAlexaboutno aff
Katrin Kohnert, Andrei Serafimovich, Stefan Metzger, Jörg Hartmann, Torsten Sachs

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsPermafrostMethaneArcticEarth scienceDeltaGeologyEnvironmental sciencePhysical geographyClimate changeHydrology (agriculture)OceanographyEcologyGeography

Abstract

fetched live from OpenAlex

Abstract Arctic permafrost caps vast amounts of old, geologic methane (CH 4 ) in subsurface reservoirs. Thawing permafrost opens pathways for this CH 4 to migrate to the surface. However, the occurrence of geologic emissions and their contribution to the CH 4 budget in addition to recent, biogenic CH 4 is uncertain. Here we present a high-resolution (100 m × 100 m) regional (10,000 km²) CH 4 flux map of the Mackenzie Delta, Canada, based on airborne CH 4 flux data from July 2012 and 2013. We identify strong, likely geologic emissions solely where the permafrost is discontinuous. These peaks are 13 times larger than typical biogenic emissions. Whereas microbial CH 4 production largely depends on recent air and soil temperature, geologic CH 4 was produced over millions of years and can be released year-round provided open pathways exist. Therefore, even though they only occur on about 1% of the area, geologic hotspots contribute 17% to the annual CH 4 emission estimate of our study area. We suggest that this share may increase if ongoing permafrost thaw opens new pathways. We conclude that, due to permafrost thaw, hydrocarbon-rich areas, prevalent in the Arctic, may see increased emission of geologic CH 4 in the future, in addition to enhanced microbial CH 4 production.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.046
GPT teacher head0.265
Teacher spread0.219 · 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 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

Citations76
Published2017
Admission routes1
Has abstractyes

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