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Record W3136175182 · doi:10.1029/2020gb006684

Stable Carbon Isotopes Suggest Large Terrestrial Carbon Inputs to the Global Ocean

2021· article· en· W3136175182 on OpenAlexaff
Eun Young Kwon, Tim DeVries, Eric D. Galbraith, Jeomshik Hwang, Guebuem Kim, Axel Timmermann

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsCarbon cycleEnvironmental scienceTerrestrial ecosystemGlobal changeCarbon fibersIsotopes of carbonOceanographyCarbon fluxEcosystemClimate changeTotal organic carbonGeologyEcologyEnvironmental chemistryChemistry

Abstract

fetched live from OpenAlex

Abstract The transport of carbon from land to ocean, via rivers, groundwater, and aerosols, is an important component of the global carbon cycle that must be known to accurately assess anthropogenic CO 2 storage on land and in the ocean. Current global carbon cycle budgets have adopted terrestrial carbon inputs to the ocean ranging from 0.5 GtC/yr to 0.9 GtC/yr, derived mainly from estimates of riverine fluxes. However, these budgets ignore the terrestrial carbon inputs from coastal ecosystems and through submarine groundwater discharge (collectively referred to as coastal margin inputs in this study) due to difficulties in making global assessments. Using a numerical model and globally distributed ocean observations of stable carbon isotopes, we estimate terrestrial carbon inputs to the ocean at 1.4 ± 0.5 GtC/yr, with 95% of the coastal margin inputs (0.8 ± 0.5 GtC/yr) occurring in the Pacific and Indian Oceans. This terrestrial carbon flux is largely balanced by an efflux to the atmosphere of 1.2 ± 0.5 GtC/yr, 40% of which occurs in poorly monitored coastal regions and may have been overlooked in previous observation‐based global estimates. Our results suggest more dynamic cycling of carbon in the land‐ocean transition zone than previously thought, and that rivers may not be the only important pathway for terrestrial carbon to the ocean.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.006
GPT teacher head0.215
Teacher spread0.209 · 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

Citations41
Published2021
Admission routes1
Has abstractyes

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