Variation and control of soil organic carbon and other nutrients in permafrost regions on central Qinghai-Tibetan Plateau
Bibliographic record
Abstract
The variation and control of soil organic carbon (SOC) and other nutrients in permafrost regions are critical for studying the carbon cycle and its potential feedbacks to climate change; however, they are poorly understood. Soil nutrients samples at depths of 0–10, 10–20, 20–30, and 30–40 cm, were sampled eight times in 2009 in alpine swamp meadow, alpine meadow and alpine steppe in permafrost regions of the central Qinghai-Tibetan Plateau. SOC and total nitrogen (TN) in the alpine swamp meadow and meadow decreased with soil depth, whereas the highest SOC content in the alpine steppe was found at depths of 20–30 cm. The vertical profiles of total and available phosphorus (P) and potassium (K) were relatively uniform for all the three grassland types. Correlation and linear regression analyses showed that soil moisture (SM) was the most important parameter for the vertical variation of SOC and other soil nutrients, and that belowground biomass (BGB) was the main source of SOC and TN. The spatial variations (including seasonal variation) of SOC and TN at plot scale were large. The relative deviation of SOC ranged from 7.18 to 41.50 in the alpine swamp meadow, from 2.88 to 35.91 in the alpine meadow, and from 9.33 to 68.38 in the alpine steppe. The spatial variations in the other soil nutrients varied among different grassland types. The most important factors for spatial variations (including seasonal variation) of SOC, TN, total P, available P, and both total and available K were: SM, SM and temperature, SM, air temperature, and SM and BGB, respectively. The large variation in the three grassland types implies that spatial variation at plot scale should be considered when estimating SOC storage and its dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".