Decadal shifts in Qingzang Plateau lake carbon dynamics (1970–2020): From predominant carbon sources to emerging sinks
Bibliographic record
Abstract
The evasion of carbon dioxide (CO2) from lakes significantly influences the global carbon equilibrium. Amidst global climatic transformations, the role of Qingzang Plateau (QZP) lakes as carbon (C) sources or sinks remains a subject of debate. Furthermore, accurately quantifying their contribution to the global carbon budget presents a formidable challenge. Here, spanning half a century (1970–2020), we utilize a synthesis of literature and empirical field data to assess the CO2 exchange flux of QZP lakes. We find markedly higher CO2 exchange flux in the southeast lakes than that in the northern and western regions from 1970 to 2000. During this time, both freshwater and saltwater lakes served primarily as carbon sources. The annual CO2 exchange flux was estimated at 2.04 ± 0.37 Tg (Tg) C yr−1, mainly influenced by temperature fluctuations. The CO2 exchange flux patterns underwent a geographical inversion between 2000 and 2020, with increased levels in the west and decreased levels in the east. Notably, CO2 emissions from freshwater lakes diminished, and certain saltwater lakes in the QTP transitioned from carbon sources to sinks. From 2000 to 2020, the annual CO2 exchange flux from QZP lakes is estimated at 1.34 ± 0.50 Tg C yr−1, with solar radiation playing a more pronounced role in carbon emissions. Cumulatively, over the past five decades, QZP lakes have generally functioned as carbon sources. Nevertheless, the total annual CO2 emissions have declined since the year 2000, indicating a potential shift trend from being a carbon source to a sink, mirroring broader patterns of global climate change. These findings not only augment our understanding of the carbon cycle in plateau aquatic systems but also provide crucial data for refining China's carbon budget.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".