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Record W4414287865 · doi:10.1016/j.jia.2025.02.007

Surface soil organic carbon losses in Dongting Lake floodplain as evidenced by field observations from 2013 to 2022

2025· article· en· W4414287865 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Integrative Agriculture · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité du Québec à Montréal
FundersNational Key Research and Development Program of ChinaYouth Innovation Promotion Association of the Chinese Academy of SciencesMinistry of Water ResourcesNational Natural Science Foundation of China
KeywordsFloodplainSoil carbonHydrology (agriculture)Climate changeTotal organic carbonSoil horizonCarbon sequestrationSoil water

Abstract

fetched live from OpenAlex

• The surface soil organic carbon stock of Dongting Lake in 2022 was 6.82 Tg C (2.87–13.48 Tg C). • Surface SOC was lost in Dongting Lake because of climatic and hydrological changes. • Above 21.43 m elevation, SOC loss accelerated with increasing elevation. • Raising the water level during drought periods may be an important way to enhance the carbon sequestration potential of wetlands. In floodplain wetlands, alterations in hydrological patterns resulting from climate change and human activities could potentially diminish the carbon sequestration capacity of the soils, thereby having a negative impact on global climate change. However, the magnitude of the influence of hydrological regime change on soil carbon remains inadequately monitored. To address this research gap, we collected 306 upper layer (0-20 cm) soil samples from the Dongting Lake floodplain between 2013 and 2022. The Random Forest (RF) algorithm was used to analyze the spatial distribution of soil organic carbon (SOC) in the upper soil layer of Dongting Lake floodplain and the impact of climate and hydrological changes in the past decade on surface SOC in the East Dongting Lake area was studied. In 2022, the SOC concentration of the Dongting Lake floodplain upper layer soil ranged from 3.34 to 17.67 g kg −1 , averaging 10.43 g kg −1 , with a corresponding SOC density of 2.65±0.49 kg m −2 and total SOC stock of 6.82 Tg C (2.87–13.48 Tg C). From 2013 to 2022, the SOC concentration of the upper soil layer of the East Dongting Lake area decreased from 18.37 g kg −1 to 10.82 g kg −1 . This reduction could be attributed to climate and hydrological changes which reduce SOC input by reducing vegetation growth and accelerating SOC decomposition. Above 21.4 m elevation, the amount of SOC loss increased with elevation, the loss being related to the decline in Miscanthus community biomass and greater susceptibility of higher altitude areas to climate and hydrological changes. Our results highlight the need for strengthening wetland SOC management to increase SOC in the soils to help combat climate change.

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.

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)0.001
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 score0.425
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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