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Record W3092797288 · doi:10.5194/cp-17-1627-2021

CH <sub>4</sub> and N <sub>2</sub> O fluctuations during the penultimate deglaciation

2021· article· en· W3092797288 on OpenAlexaff
Loïc Schmidely, Christoph Nehrbass‐Ahles, J. H. M. M. Schmitt, Juhyeong Han, Lucas Silva, Jinwha Shin, Fortunat Joos, J. Chappellaz, Hubertus Fischer, Thomas F. Stocker

Bibliographic record

VenueClimate of the past · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Alberta
FundersHorizon 2020
KeywordsDeglaciationStadialIce coreGlacial periodGeologyOceanographyGeomorphology

Abstract

fetched live from OpenAlex

Abstract. Deglaciations are characterized by the largest natural changes in methane (CH4) and nitrous oxide (N2O) concentrations of the past 800 000 years. Reconstructions of millennial- to centennial-scale variability within these periods are mostly restricted to the last deglaciation. In this study, we present composite records of CH4 and N2O concentrations from the EPICA Dome C ice core covering the penultimate deglaciation at temporal resolutions of ∼100 years. Our data permit the identification of centennial-scale fluctuations during the transition from glacial to interglacial levels. At ∼134 000 and ∼129 000 years before present (hereafter ka), both CH4 and N2O increased on centennial timescales. These abrupt rises are similar to the fluctuations associated with the Dansgaard–Oeschger events identified in the last glacial period. In addition, gradually rising N2O levels at ∼130 ka resemble a pattern of increasing N2O concentrations on millennial timescales characterizing the later part of Heinrich stadials. Overall, the events in CH4 and N2O during the penultimate deglaciation exhibit modes of variability that are also found during the last deglaciation and glacial cycle, suggesting that the processes leading to changes in emission during the transitions were similar but their timing differed.

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 score0.046
Threshold uncertainty score0.636

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 studies0.0010.000
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 head0.013
GPT teacher head0.225
Teacher spread0.213 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

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