MétaCan
Menu
Back to cohort
Record W2158450675 · doi:10.1088/1748-9326/8/3/035020

Empirical estimates to reduce modeling uncertainties of soil organic carbon in permafrost regions: a review of recent progress and remaining challenges

2013· review· en· W2158450675 on OpenAlexaff
Umakant Mishra, Julie Jastrow, Roser Matamala, Gustaf Hugelius, Charles D. Koven, J. W. Harden, Chien‐Lu Ping, G. J. Michaelson, Zeng Fan, R. M. Miller, A. David McGuire, C. Tarnocai, Peter Kuhry, W. J. Riley, Kevin Schaefer, Edward A. G. Schuur, M. Torre Jorgenson, L. D. Hinzman

Bibliographic record

VenueEnvironmental Research Letters · 2013
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsAgriculture and Agri-Food Canada
FundersArgonne National LaboratoryBiological and Environmental ResearchOffice of ScienceNational Science Foundation
KeywordsPermafrostEnvironmental scienceSoil carbonLimitingSoil waterGreenhouse gasCarbon cycleCarbon fibersPhysical geographyClimatologyCircumpolar starEarth scienceAtmospheric sciencesSoil scienceGeologyEcologyGeographyEcosystem

Abstract

fetched live from OpenAlex

The vast amount of organic carbon (OC) stored in soils of the northern circumpolar permafrost region is a potentially vulnerable component of the global carbon cycle. However, estimates of the quantity, decomposability, and combustibility of OC contained in permafrost-region soils remain highly uncertain, thereby limiting our ability to predict the release of greenhouse gases due to permafrost thawing. Substantial differences exist between empirical and modeling estimates of the quantity and distribution of permafrost-region soil OC, which contribute to large uncertainties in predictions of carbon–climate feedbacks under future warming. Here, we identify research challenges that constrain current assessments of the distribution and potential decomposability of soil OC stocks in the northern permafrost region and suggest priorities for future empirical and modeling studies to address these challenges.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.258
GPT teacher head0.392
Teacher spread0.134 · 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 designOther design
Domainnot available
GenreReview

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

Citations82
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Research LettersSame topicClimate change and permafrostFrench-language works237,207