Assessment of frozen ground organic carbon pool on the Qinghai-Tibet Plateau
Classification
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".
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.
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.
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.
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) | 0.002 | 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 it