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Record W4401032351 · doi:10.1038/s41558-024-02057-4

Decadal increases in carbon uptake offset by respiratory losses across northern permafrost ecosystems

2024· article· en· W4401032351 on OpenAlexafffund
Craig R. See, Anna‐Maria Virkkala, Susan M. Natali, Brendan M. Rogers, Marguerite Mauritz, Christina Biasi, Stef Bokhorst, Julia Boike, M. Syndonia Bret‐Harte, Gerardo Celis, Namyi Chae, Torben R. Christensen, Sara June Murner, Sigrid Dengel, A. J. Dolman, Colin W. Edgar, Bo Elberling, Craig A. Emmerton, E. S. Euskirchen, Mathias Göckede, Achim Grelle, Liam Heffernan, Manuel Helbig, David Holl, Elyn Humphreys, Hiroki Iwata, Järvi Järveoja, Hideki Kobayashi, John Kochendorfer, Pasi Kolari, Ayumi Kotani, Lars Kutzbach, Min Jung Kwon, Emma Lathrop, Efrèn López‐Blanco, Ivan Mammarella, Maija E. Marushchak, Mikhail Mastepanov, Yojiro Matsuura, Lutz Merbold, Gesa Meyer, Christina Minions, Mats B. Nilsson, Julia Nojeim, Steven F. Oberbauer, David Olefeldt, Sang‐Jong Park, Frans‐Jan W. Parmentier, Matthias Peichl, D. L. Peter, Roman Petrov, Rafael Poyatos, Anatoly Prokushkin, William L. Quinton, Heidi Rodenhizer, Torsten Sachs, K. E. Savage, Christopher Schulze, Sofie Sjögersten, Oliver Sonnentag, Vincent L. St. Louis, Margaret Torn, Eeva‐Stiina Tuittila, Masahito Ueyama, Andrej Varlagin, Carolina Voigt, Jennifer D. Watts, Donatella Zona, Viacheslav I. Zyryanov, Edward A. G. Schuur

Bibliographic record

VenueNature Climate Change · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsWilfrid Laurier UniversityEnvironment and Climate Change CanadaCarleton UniversityDalhousie UniversityUniversité de MontréalUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaOffice of ScienceHORIZON EUROPE Framework ProgrammeU.S. Geological SurveyMinderoo FoundationNorges ForskningsrådVetenskapsrådetDeutsche ForschungsgemeinschaftKorea Polar Research InstituteBundesministerium für Bildung und ForschungNational Research FoundationU.S. Department of EnergyEuropean CommissionDanmarks GrundforskningsfondNational Aeronautics and Space AdministrationHelsingin YliopistoEuropean Climate, Infrastructure and Environment Executive AgencyGordon and Betty Moore FoundationNational Science Foundation
KeywordsPermafrostEnvironmental scienceEcosystemCarbon cycleOffset (computer science)Carbon fibersPhysical geographyAtmospheric sciencesOceanographyEcologyGeologyGeographyBiology

Abstract

fetched live from OpenAlex

Abstract Tundra and boreal ecosystems encompass the northern circumpolar permafrost region and are experiencing rapid environmental change with important implications for the global carbon (C) budget. We analysed multi-decadal time series containing 302 annual estimates of carbon dioxide (CO 2 ) flux across 70 permafrost and non-permafrost ecosystems, and 672 estimates of summer CO 2 flux across 181 ecosystems. We find an increase in the annual CO 2 sink across non-permafrost ecosystems but not permafrost ecosystems, despite similar increases in summer uptake. Thus, recent non-growing-season CO 2 losses have substantially impacted the CO 2 balance of permafrost ecosystems. Furthermore, analysis of interannual variability reveals warmer summers amplify the C cycle (increase productivity and respiration) at putatively nitrogen-limited sites and at sites less reliant on summer precipitation for water use. Our findings suggest that water and nutrient availability will be important predictors of the C-cycle response of these ecosystems to future warming.

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)0.001
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.034
GPT teacher head0.286
Teacher spread0.252 · 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

Citations50
Published2024
Admission routes2
Has abstractyes

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