Spatial variability of active layer thickness detected by ground‐penetrating radar in the Qilian Mountains, Western China
Bibliographic record
Abstract
Abstract The active layer plays a key role in geomorphic, hydrologic, and biogeochemical processes in permafrost regions. We conducted a systematic investigation of active layer thickness (ALT) in northeastern Qinghai‐Tibetan Plateau by using ground‐penetrating radar (GPR) with 100 and 200 MHz antennas. We used mechanical probing, pit, and soil temperature profiles for evaluating ALT derived from GPR. The results showed that GPR is competent for detecting ALT, and the error was ±0.08 m at common midpoint co‐located sites. Considerable spatial variability of ALT owing to variation in elevation, peat thickness, and slope aspect was found. The mean ALT was 1.32 ± 0.29 m with a range from 0.81 to 2.1 m in Eboling Mountain. In Yeniu Gou, mean ALT was 2.72 ± 0.88 m and varied from 1.07 m on the north‐facing slope to 4.86 m around the area near the lower boundary of permafrost. ALT in peat decreased with increasing elevation at rates of −1.31 m/km (Eboling Mountain) and −2.1 m/km (Yeniu Gou), and in mineral soil in Yeniu Gou, the rate changed to −4.18 m/km. At the same elevation, ALT on the south‐facing slope was about 0.8 m thicker than that on the north‐facing slopes, while the difference was only 0.18 m in peat‐covered area. Within a 100 m 2 area with a local elevation difference of 0.8 m, ALT varied from 0.68 m to 1.25 m. Both field monitoring and modeling studies on spatial ALT variations require rethinking of the current strategy and comprehensive design.
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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) | 0.000 | 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".