Assessment of frozen ground organic carbon pool on the Qinghai-Tibet Plateau
Bibliographic record
Abstract
Under rapid climate change, soil organic carbon (SOC) dynamic in frozen ground may significantly influence terrestrial carbon cycles. The aim of this study was to investigate the storage, spatial patterns, and influencing factors of SOC in frozen ground on the Qinghai-Tibet Plateau, which known as the earth’s Third Pole. Using the observed edaphic data from China’s Second National Soil Survey, we estimated the SOC storage (SOCS) of frozen ground (including permafrost, seasonally, and short time frozen ground) on the plateau with a depth of 0–3 m. Furthermore, the effect of vegetation and climate factors on spatial variance of SOC density (SOCD) was analyzed. The SOCD decreased from the southeastern to the northwestern part of the plateau, and increased with shorten of freezing duration. SOCS of permafrost, seasonally, and short time frozen ground were calculated as 40.9 (34.2–47.6), 26.7 (24.1–29.4), and 6 (5.6–6.4) Pg, making a total of 73.6 (63.9–83.3) Pg in 0–3 m depth on the plateau. Normalized difference vegetation index and mean annual precipitation could significantly affect the spatial distribution of SOC in permafrost and seasonally frozen ground. The soil in plateau frozen ground contained substantial organic carbon, which could be affected by plant and climate variables. However, the heterogeneous landform may make the fate of carbon more complicated in the future.
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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 | 0.001 | 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".