Quantification of the Loess Plateau's soil hydrodynamics in relation to bulk density
Bibliographic record
Abstract
Abstract Investigating the effects of varying degrees of soil compaction on its hydrodynamic properties is still a vital step in optimizing water utilization. Furthermore, hydrodynamic parameters such as saturated hydraulic conductivity (Ks) and soil water retention characteristics (SWRC) are essential data for soil water and solute transport calculations. However, it takes a lot of time and money to get direct measurements of hydrodynamic properties. The purpose of this study was to measure how the Loess Plateau's SWRC, Ks, and soil pores were affected by five different degrees of bulk density (BD), and quantify interactions between BD, soil organic carbon (SOC), and particle size distribution (PSD) on hydrodynamic parameters using pedotransfer function (PTF). Hydrodynamic parameters were predicted using multiple linear regression (MLR), and the best models were chosen using statistical standards and compared with Rosetta3 models based on predictors % sand, silt, and clay (SSC) and SSC+BD. The results showed that increasing soil BD from 1.00 to 1.40 g cm −3 led to significant reductions in soil saturated water content (SSAT), quickly draining pores (QDP), and Ks. Enhances SOC content and clay from micropores under BD, and low SOC soil suffers pore collapse. MLR model‐based (BD+SOC) predicted hydrodynamic parameters, and the models demonstrated that “BD+SOC” is the best combination. MLR‐BD+SOC model outperformed (root mean square error [RMSE]: 0.001–0.005; and R 2 : 0.91–0.98) on Rosetta3 models. The Rosetta3‐SSC+BD model improved predictions in low‐SOC soils but underperformed in SOC‐rich soils. These findings emphasize integrating BD and SOC in PTF for accurate hydrodynamic modeling, particularly in erosion‐prone, heterogeneous landscapes.
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 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 | 0.000 | 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) | 0.001 | 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".