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Record W3161972058 · doi:10.1029/2021ef002124

Estimation of Oceanic and Land Carbon Sinks Based on the Most Recent Oxygen Budget

2021· article· en· W3161972058 on OpenAlexaff
Changyu Li, Jianping Huang, Lei Ding, Xiaoyue Liu, Dongliang Han, Jiping Huang

Bibliographic record

VenueEarth s Future · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsBioSci Research Canada (Canada)
FundersChinese Academy of Sciences
KeywordsCarbon cycleCarbon sinkEnvironmental scienceCarbon fibersCarbon fluxEarth system scienceClimate changeGreenhouse gasClimatologyAtmospheric sciencesOceanographyEcologyGeologyBiologyComputer scienceEcosystem

Abstract

fetched live from OpenAlex

Abstract Robust assessments of global carbon uptake are important for understanding Earth's carbon cycle and its response to human impacts. Here, based on the most recent oxygen budget, we presented an alternative estimate of ocean and land carbon sinks over the past few decades and future projections under climate change. For the period from 1990 to 2015, the ocean and land carbon sinks were ∼2.16 ± 0.73 and 1.37 ± 0.91 GtC/yr, respectively, which are in good agreement with the results from the Global Carbon Project (GCP). Our estimated temporal evolution of oceanic carbon uptake, however, presents a stronger decadal variation than the quasi‐monotonous increase estimated by the GCP. Future projections of carbon sinks show significant discrepancies under different scenarios. At the end of this century, the ocean and land sinks will be 2.96 and 0.75 GtC/yr, respectively, under RCP4.5 (representative concentration pathways), while these values will be much larger under RCP8.5 at ∼5.70 and 3.69 GtC/yr, highlighting the vital role of the human‐induced influence on the carbon cycle.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.174
Teacher spread0.171 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations16
Published2021
Admission routes1
Has abstractyes

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