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Record W3156004002 · doi:10.1038/s41467-021-22452-1

Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

2021· article· lv· W3156004002 on OpenAlexaff
Kuang‐Yu Chang, W. J. Riley, Sara Knox, Robert B. Jackson, Gavin McNicol, Benjamin Poulter, Mika Aurela, Dennis Baldocchi, Sheel Bansal, Gil Bohrer, David I. Campbell, Alessandro Cescatti, Housen Chu, Kyle Delwiche, Ankur R. Desai, E. S. Euskirchen, Thomas Friborg, Mathias Goeckede, Manuel Helbig, Kyle S. Hemes, Takashi Hirano, Hiroki Iwata, Minseok Kang, Trevor F. Keenan, Ken W. Krauss, Annalea Lohila, Ivan Mammarella, Bhaskar Mitra, Akira Miyata, Mats B. Nilsson, Asko Noormets, Walter C. Oechel, Dario Papale, Matthias Peichl, Michele L. Reba, Janne Rinne, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Karina V. R. Schäfer, Hans Peter Schmid, Narasinha Shurpali, Oliver Sonnentag, Angela Che Ing Tang, Margaret Torn, Carlo Trotta, Eeva‐Stiina Tuittila, Masahito Ueyama, Rodrigo Vargas, Timo Vesala, Lisamarie Windham‐Myers, Zhen Zhang, Donatella Zona

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languagelv
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMcMaster UniversityCenter for Northern StudiesUniversity of British Columbia
FundersBiological and Environmental ResearchNatural Environment Research CouncilJapan Society for the Promotion of ScienceHorizon 2020 Framework ProgrammeOffice of ScienceU.S. Geological SurveyDipartimento per la Innovazione nei Sistemi Biologici, Agroalimentari e Forestali, Università degli Studi della TusciaNational Research FoundationNational Oceanic and Atmospheric AdministrationSight Research UKDipartimenti di EccellenzaNOAA Center for Earth System Sciences and Remote Sensing TechnologiesNational Research Foundation of KoreaU.S. Department of EnergyGordon and Betty Moore FoundationNational Aeronautics and Space AdministrationVetenskapsrådetNational Science Foundation
KeywordsAlgorithmComputer science

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.259
Teacher spread0.249 · 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

Citations102
Published2021
Admission routes1
Has abstractyes

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