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Record W4414104328 · doi:10.1016/j.fmre.2025.08.012

Determining carbon fate and budgets throughout the Yangtze mainstream’s transportation processes

2025· article· en· W4414104328 on OpenAlexfundno aff
Mingrui Wang, Junjie Jia, Changchun Huang, Fan Wu, Kun Sun, Shuoyue Wang, Yang Gao

Bibliographic record

VenueFundamental Research · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersChinese Academy of SciencesNational Natural Science Foundation of ChinaMcGill University
KeywordsCarbon fibersYangtze riverCarbon cycleWork (physics)Climate change

Abstract

fetched live from OpenAlex

It is critical to comprehensively and accurately assess large-scale river carbon (C) fluxes and budgets for reducing global C budget uncertainty, particularly in rivers where extensive dam construction has occurred. However, there is a lack of integrative research on multi-interface fluxes extending from headwaters to estuaries, as well as associated influencing mechanisms. This study addressed the fate of different C components during riverine transport and identified their primary driving factors, elucidating the C budget dynamics pre-dam and post-dam construction. Results showed that the Yangtze mainstem acts as a biogeochemical reactor, with 13.5 Tg C/yr transported to the East China Sea (ECS), 4.7 Tg C/yr emitted, and 4.4 Tg C/yr buried. Hydrological processes, physicochemical parameters (temperature, pH, and total suspended matter) and dam construction governed the lateral C transportation. Nutrient concentrations and ratios influenced C emissions and burial processes. The Three Gorges Dam (TGD) increases C residence time, promotes the transformation of dissolved to particulate C, and enhances C retention, ultimately boosting riverine C burial by 140 %. Meanwhile, by altering the carbonate system, TGD elevates the pH and reduces pCO₂ in the Yangtze mainstem, decreasing its C source effect by 66 %. These findings are crucial for accurately quantifying the C budget of the Yangtze mainstem and forecasting its responses to anthropogenic pressures and dynamic climatic conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.040
GPT teacher head0.338
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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