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Record W4309468144 · doi:10.1126/sciadv.add0610

Global fjords as transitory reservoirs of labile organic carbon modulated by organo-mineral interactions

2022· article· en· W4309468144 on OpenAlexaff
Xingqian Cui, Alfonso Mucci, Thomas S. Bianchi, Ding He, Derrick Vaughn, Elizabeth K. Williams, Chuning Wang, Craig Smeaton, Katarzyna Koziorowska‐Makuch, Johan C. Faust, Alain F. Plante, B. E. Rosenheim

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsMcGill University
Fundersnot available
KeywordsFjordGlacial periodCarbon cycleTotal organic carbonEnvironmental chemistryOceanographyGeologySedimentationCarbon fibersSedimentary rockBiogeochemical cycleGeochemistryEnvironmental scienceChemistryGeomorphologySedimentEcosystemEcologyMaterials scienceBiology

Abstract

fetched live from OpenAlex

The global carbon cycle is strongly modulated by organic carbon (OC) sequestration and decomposition. Whereas OC sequestration is relatively well constrained, there are few quantitative estimates of its susceptibility to decomposition. Fjords are hot spots of sedimentation and OC sequestration in marine sediments. Here, we adopt fjords as model systems to investigate the reactivity of sedimentary OC by assessing the distribution of the activation energy required to break OC bonds. Our results reveal that OC in fjord sediments is more thermally labile than that in global sediments, which is governed by its unique provenance and organo-mineral interactions. We estimate that 61 ± 16% of the sedimentary OC in fjords is degradable. Once this OC is remobilized and remineralized during glacial maxima, the resulting metabolic CO 2 could counterbalance up to 50 ppm of the atmospheric CO 2 decrease during glacial times, making fjords critical actors in dampening glacial-interglacial climate fluctuations through negative carbon cycling loops.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.005
GPT teacher head0.239
Teacher spread0.235 · 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 designBench or experimental
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

Citations71
Published2022
Admission routes1
Has abstractyes

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