Hydraulic Properties Within the Complete Moisture Range of Hydric Soil on the Tibetan Plateau
Bibliographic record
Abstract
Abstract The Tibetan Plateau is well‐known for its expansive wetland environments. Hydric soils, a fundamental component of these environments, exhibit diverse hydraulic characteristics attributable to their varied botanical and mineralogical composition and their inherent porous structures. Nonetheless, research on the hydraulic properties of such soils in Tibet remains notably underrepresented relative to European and Canadian regions. Consequently, in this study, we evaluate the effectiveness of different unsaturated hydraulic schemes in equilibrium and examine the parameter uncertainty of 14 undisturbed samples collected from four soligenous wetlands. The findings suggest that both the van Genuchten and Kosugi functions, when integrated with the Peters‐Iden‐Durner (PDI) model, yield a nearly consistent fit to experimental observations and demonstrate strong identifiability of parameters. This indicates that the PDI model can accurately characterize hydraulic properties across the complete moisture range of hydric soils. Analysis of samples with a low clay content and no sphagnum suggests that the intertwined, twisted, and hollow residues of herbaceous vascular tissues do not create a distinct, independent macro‐pore system. Therefore, the unimodal scheme integrating the PDI model is adequate. However, for samples that exhibit nonmonotonicity of the first‐order derivative of the retention curve, such as uncompacted samples containing sphagnum or samples rich in clay, the integration of the PDI model into the bimodal scheme boosts accuracy while having almost negligible impact on identifiability. The varied observed hydraulic properties of only 14 samples underscore the necessity for extensive hydric‐soil sampling and hydraulic analysis across the expansive and varied wetland landscapes on the Tibetan Plateau.
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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 | 0.001 | 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".