Response of changes in seasonal soil freeze/thaw state to climate change from 1950 to 2010 across china
Bibliographic record
Abstract
Abstract Variations in seasonal soil freeze/thaw state are important indicators of climate change and influence ground temperature, hydrological processes, surface energy, and the moisture balance. Previous studies mainly focused on the active layer and permafrost, while seasonally frozen ground research in nonpermafrost regions has received less attention. In this study, we investigate the response of changes in seasonal soil freeze/thaw state to changes in air temperatures by combining observations from more than 800 stations with gridded mean monthly air temperature data across China. The results show that mean annual air temperature (MAAT) increased statistically significantly by 0.29 ± 0.03°C/decade from 1967 to 2013, with greater warming on the Qinghai‐Tibetan Plateau. There is a statistically significant decrease in the freeze/thaw cycle (FTC) at 0.39 ± 0.05 cycles/decade. In addition, there are strong negative correlations between FTC and MAAT. Estimating the soil freeze/thaw state classification based on the number of days in the month, we find that changes of mean annual area extent of seasonal soil freeze/thaw state decreased significantly for completely frozen (CF) ground, while the area extent of partially frozen (PF) and unfrozen (UF) ground both increased. Changes in mean monthly area extent of seasonal soil freeze/thaw state indicate that the extent of CF and UF area was decreasing and increasing, respectively. But for the extent of PF areas, both increasing and decreasing trends were observed. Quantifying the spatial pattern of the seasonal soil freeze/thaw, we find that CF and PF areas are located in northern China and the Tibetan Plateau from December to March, and UF areas are located in southern China. The variations of mean annual area extent departure of soil freeze/thaw states are consistent with MAAT changes in different land cover types across 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 | 0.001 |
| 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".