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Record W2130740562 · doi:10.5194/bg-12-4841-2015

Multi-molecular tracers of terrestrial carbon transfer across the pan-Arctic: comparison of hydrolyzable components with plant wax lipids and lignin phenols

2015· article· en· W2130740562 on OpenAlexafffund
Xiaojuan Feng, Örjan Gustafsson, R. M. Holmes, Jorien E. Vonk, Bart E. van Dongen, Igor Semiletov, Oleg Dudarev, Mark B. Yunker, Robie W. Macdonald, Daniel B. Montluçon, Timothy I. Eglinton

Bibliographic record

VenueBiogeosciences · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsFisheries and Oceans Canada
FundersRussian Academy of SciencesNatural Environment Research CouncilFisheries and Oceans CanadaEidgenössische Technische Hochschule ZürichNordisk MinisterrådKnut och Alice Wallenbergs StiftelseSight Research UKNational Oceanic and Atmospheric AdministrationNederlandse Organisatie voor Wetenschappelijk OnderzoekPolarforskningssekretariatetWoods Hole Oceanographic InstitutionSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungVetenskapsrådetNational Science Foundation
KeywordsCutinSuberinLigninChemistryPhenolsWaxEnvironmental chemistryContext (archaeology)BotanyOrganic matterArcticOrganic chemistryEcologyGeologyBiologyBiochemistryPaleontology

Abstract

fetched live from OpenAlex

Abstract. Hydrolyzable organic carbon (OC) comprises a significant component of sedimentary particulate matter transferred from land into oceans via rivers. Its abundance and nature are however not well studied in Arctic river systems, and yet may represent an important pool of carbon whose fate remains unclear in the context of mobilization and related processes associated with a changing climate. Here, we examine the molecular composition and source of hydrolyzable compounds isolated from sedimentary particles derived from nine rivers across the pan-Arctic. Bound fatty acids (b-FAs), hydroxy FAs, n-alkane-α,ω-dioic acids (DAs) and phenols were the major components released upon hydrolysis of these sediments. Among them, b-FAs received considerable inputs from bacterial and/or algal sources, whereas ω-hydroxy FAs, mid-chain substituted acids, DAs, and hydrolyzable phenols were mainly derived from cutin and suberin of higher plants. We further compared the distribution and fate of suberin- and cutin-derived compounds with those of other terrestrial biomarkers (plant wax lipids and lignin phenols) from the same Arctic river sedimentary particles and conducted a benchmark assessment of several biomarker-based indicators of OC source and extent of degradation. While suberin-specific biomarkers were positively correlated with plant-derived high-molecular-weight (HMW) FAs, lignin phenols were correlated with cutin-derived compounds. These correlations suggest that, similar to leaf-derived cutin, lignin was mainly derived from litter and surface soil horizons, whereas suberin and HMW FAs incorporated significant inputs from belowground sources (roots and deeper soil). This conclusion is supported by the negative correlation between lignin phenols and the ratio of suberin-to-cutin biomarkers. Furthermore, the molecular composition of investigated biomarkers differed between Eurasian and North American Arctic rivers: while lignin dominated in the terrestrial OC of Eurasian river sediments, hydrolyzable OC represented a much larger fraction in the sedimentary particles from Colville River. Hence, studies exclusively focusing on either plant wax lipids or lignin phenols will not be able to fully unravel the mobilization and fate of bound OC in Arctic rivers. More comprehensive, multi-molecular investigations are needed to better constrain the land–ocean transfer of carbon in the changing Arctic, including further research on the degradation and transfer of both free and bound components in Arctic river sediments.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.981

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.281
Teacher spread0.203 · 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 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

Citations37
Published2015
Admission routes2
Has abstractyes

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