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Record W4387164581 · doi:10.1126/sciadv.adh9704

Aquatic plant wax hydrogen and carbon isotopes in Greenland lakes record shifts in methane cycling during past Holocene warming

2023· article· en· W4387164581 on OpenAlexaff
Jamie McFarlin, Yarrow Axford, Stephanie Kusch, Andrew L. Masterson, G. E. Lasher, Magdalena R. Osburn

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversité du Québec à Rimouski
FundersDivision of Polar ProgramsU.S. Air ForceOffice of Polar ProgramsNorthwestern UniversityNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsHoloceneCyclingMethaneEnvironmental scienceCarbon cycleIsotopes of carbonCarbon fibersOceanographyWaxGlobal warmingEnvironmental chemistryClimate changeEcologyPhysical geographyGeologyChemistryEcosystemTotal organic carbonGeographyArchaeologyBiologyMaterials science

Abstract

fetched live from OpenAlex

Predicting changes to methane cycling in Arctic lakes is of global concern in a warming world but records constraining lake methane dynamics with past warming are rare. Here, we demonstrate that the hydrogen isotopic composition (δ 2 H) of mid-chain waxes derived from aquatic moss clearly decouples from precipitation during past Holocene warmth and instead records incorporation of methane in plant biomass. Trends in δ 2 H moss and δ 13 C moss values point to widespread Middle Holocene (11,700 to 4200 years ago) shifts in lake methane cycling across Greenland during millennia of elevated summer temperatures, heightened productivity, and lowered hypolimnetic oxygen. These data reveal ongoing warming may lead to increases in methane-derived C in many Arctic lakes, including lakes where methane is not a major component of the C cycle today. This work highlights a previously unrecognized mechanism influencing δ 2 H values of mid-chain wax and draws attention to the unquantified role of common aquatic mosses as a potentially important sink of lake methane across the Arctic.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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

Citations22
Published2023
Admission routes1
Has abstractyes

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