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Record W3157987086 · doi:10.1126/sciadv.abe5799

Increasing Pleistocene permafrost persistence and carbon cycle conundrums inferred from Canadian speleothems

2021· article· en· W3157987086 on OpenAlexafffundabout
Nicole Biller-Celander, Jeremy D. Shakun, David McGee, C. I. Wong, Alberto V. Reyes, B. F. Hardt, Irit Tal, Derek Ford, Bernard Lauriol

Bibliographic record

VenueScience Advances · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of OttawaMcMaster UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsPermafrostPleistoceneInterglacialPersistence (discontinuity)GeologyEarth scienceCarbon cyclePaleontologyClimate changePhysical geographyQuaternaryEcologyOceanographyBiologyEcosystemGeography

Abstract

fetched live from OpenAlex

Permafrost carbon represents a potentially powerful amplifier of climate change, but little is known about permafrost sensitivity and associated carbon cycling during past warm intervals. We reconstruct permafrost history in western Canada during Pleistocene interglacials from 130 uranium-thorium ages on 72 speleothems, cave deposits that only accumulate with deep ground thaw. We infer that permafrost thaw extended to the high Arctic during one or more periods between ~1.5 million and 0.5 million years ago but has been limited to the sub-Arctic since 400,000 years ago. Our Canadian speleothem growth history closely parallels an analogous reconstruction from Siberia, suggesting that this shift toward more stable permafrost across the Pleistocene may have been Arctic-wide. In contrast, interglacial greenhouse gas concentrations were relatively stable throughout the Pleistocene, suggesting that either permafrost thaw did not trigger substantial carbon release to the atmosphere or it was offset by carbon uptake elsewhere on glacial-interglacial time scales.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.999

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.236
Teacher spread0.211 · 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

Citations27
Published2021
Admission routes3
Has abstractyes

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