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Record W2889652082 · doi:10.1029/2018gl078748

Mineral Weathering and the Permafrost Carbon‐Climate Feedback

2018· article· en· W2889652082 on OpenAlexafffundabout
Scott Zolkos, Suzanne E. Tank, Steven V. Kokelj

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsGovernment of Northwest TerritoriesUniversity of Alberta
FundersEnvironment CanadaAlberta InnovatesNatural Resources CanadaUniversity of AlbertaNatural Sciences and Engineering Research Council of CanadaArctic Institute of North America
KeywordsThermokarstPermafrostWeatheringCarbonateArcticEarth scienceGeologyCarbon cycleCarbon dioxideEnvironmental chemistrySulfuric acidOcean acidificationEnvironmental scienceGeochemistryClimate changeOceanographyEcosystemChemistryEcologyInorganic chemistry

Abstract

fetched live from OpenAlex

Abstract Permafrost thaw in the Arctic enables the biogeochemical transformation of vast stores of organic carbon into carbon dioxide (CO 2 ). This CO 2 release has significant implications for climate feedbacks, yet the potential counterbalance from CO 2 fixation via chemical weathering of minerals exposed by thawing permafrost is entirely unstudied. We show that thermokarst in the western Canadian Arctic can enable rapid weathering of carbonate tills, driven by sulfuric acid from sulfide oxidation. Unlike carbonic acid‐driven weathering, this caused significant and previously undocumented CO 2 production and outgassing in headwater streams. Increasing riverine solute fluxes correspond with long‐term intensification of thermokarst and reflect the regional predominance of sulfuric acid‐driven carbonate weathering. We conclude that thermokarst‐enhanced mineral weathering has potential to profoundly disrupt Arctic freshwater carbon cycling. While thermokarst and sulfuric acid‐driven carbonate weathering in the western Canadian Arctic amplify CO 2 release, regional variation in sulfide oxidation will moderate the effects on the permafrost carbon‐climate feedback.

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 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 score0.164
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.047
GPT teacher head0.291
Teacher spread0.244 · 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

Citations89
Published2018
Admission routes3
Has abstractyes

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