Temporal‐spatial pattern of organic carbon sequestration by Chinese lakes since 1850
Bibliographic record
Abstract
Abstract In the last century, lakes in China have been subject to forcing by climate change, intensification of agriculture, and urban expansion, though their effects on lake OC sequestration are poorly understood. We compiled dry mass and OC burial rates from 82 210Pb‐dated lake sediment records in China. The average post‐1950 focusing‐corrected lake mass accumulation rate (MARFC) of 256 ± 56 g m−2 yr−1 (median ± SE) and focusing‐corrected OC accumulation rate (CARFC) of 8 ± 3 g C m−2 yr−1 were significantly higher than the 1850–1900 rates (p < 0.05). However, the magnitude of increase in CARFC was most marked in the subtropical lakes of the East Plain (EP) and on the Yunnan‐Guizhou Plateau (YG), where the post‐1950 CARFC was about three times that of the 1850–1900 (p < 0.05), due to the agricultural intensification and urban expansion in recent decades. Moreover, MARFC was significantly higher in the EP than that on the Mongolia‐Xinjiang Plateau (MX) for all time periods (p < 0.05). Lake CARFC in YG was significantly higher than rates in the Qinghai‐Tibetan Plateau (QTP) for the post‐1950 and MX for the 1850–1900 (p < 0.05). Regression analyses showed that the controls on lake CARFC varied among regions, with catchment climate variables the most important regulators in MX, Northeast China, and QTP, but the in‐lake nutrient concentrations were more important in YG and EP (p < 0.05). The results from this study show how modern limnic OC sequestration has changed with human disturbance and climate change in China.
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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".