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Record W1169721484

Mobilization of dissolved organic carbon (DOC) from permafrost due to arctic coastal erosion

2014· article· en· W1169721484 on OpenAlexaboutno aff
Michael Fritz, George Tanski, Ulrike Herzschuh, N. Couture, Thomas Opel, Hanno Meyer, Hugues Lantuit

Bibliographic record

VenueHelmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut) · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostArcticErosionDissolved organic carbonGeologyTotal organic carbonThermokarstCarbon cycleSoil carbonSea iceCoastal erosionPhysical geographyOceanographyEnvironmental scienceHydrology (agriculture)Soil waterGeomorphologySoil scienceEcosystemEcologyEnvironmental chemistryGeographyChemistryGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

Arctic permafrost coasts make up ~34% of the world’s coastline (ca. 400,000 km) and are often made of ice-rich unconsolidated sediments. This makes them highly susceptible to coastal erosion, and it is likely that large quantities of carbon are released, because permafrost soils are considered to hold approximately 50% of the global soil organic carbon pool. Current estimates of the carbon released by coastal erosion focus solely on particulate organic carbon (POC). Dissolved organic carbon (DOC) is generally not included in these calculations, because estimations of DOC contents in ground ice, which is overwhelmingly present along Arctic coasts, do not exist. In some cases, ground ice occupies as much as 90% of coastal bluffs with 40 m in height, where the coastline erodes at rates approaching 20 m/yr at its maximum. Here, we report DOC contents within permafrost from different ground ice types throughout the Arctic (Canada, Alaska, Siberia). We put them into context of Arctic organic carbon pools and fluxes, and evaluate their contribution to the Arctic carbon budget against the background of increasing permafrost degradation and enhancing coastal erosion in the future. \n \nFor example, DOC concentrations in massive ground ice bodies including ice wedges range between <1.0 and 28.6 mg/L, while ice wedges have the greatest potential as DOC pool due to their wide spatial distribution in late Pleistocene and Holocene polygonal ground. Siberian Ice Complex deposits (Yedoma) are thought to consist of up to 50% of ice wedges by volume and are therefore a substantial pool of DOC. Intrasedimental ice (non-massive) like ice lenses and pore ice are another important part of unconsolidated permafrost deposits. DOC concentrations within intrasedimental ice differ in orders of magnitude compared to massive ice and rise up to 1200 mg/L. \n \nAlthough these numbers might be still small compared to the POC stocks in peat and mineral soils, DOC is chemically labile and may directly enter local food webs of the near-shore zone. Moreover, due to its lability, DOC is quickly mineralized and returned to the atmosphere when released due to permafrost degradation. \nRobust estimations of how much organic carbon is potentially released from permafrost are crucial for predicting the strength and timing of carbon-cycle feedback mechanisms in the Arctic. This approach shall lead to an improved understanding of how important permafrost thaw in general and the erosion of permafrost coasts in particular are for the climate development this century and beyond. This is especially important in the Arctic before the background of expected rising air and sea surface temperatures, prolongation of the open-water season, increasing storm frequency and accelerating eustatic sea level rise.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.018
GPT teacher head0.242
Teacher spread0.224 · 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
Published2014
Admission routes1
Has abstractyes

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