Seasonal changes in surface bulk density and saturated hydraulic conductivity of natural landscapes
Bibliographic record
Abstract
Soil surface bulk density ( ρ b ) and saturated hydraulic conductivity ( K s) control many land‐surface processes such as water flow, chemical transport and soil erosion. The objective of this study was to examine seasonal changes in surface ρ b and K s in natural landscapes with few human activities. Measurements of ρ b and K s were made on undisturbed soil samples taken from the soil surface (0–0.05 m) five times from October 2007 to March 2009 along four natural transects in a small watershed on the Chinese Loess Plateau. The transects represented four landscapes with different vegetation and soil typical in this region. Results showed that ρ b and K s varied seasonally. Temporal changes in K s generally followed the temporal patterns of ρ b . According to the mean values of all landscapes, bulk density decreased by 1.6 and 1.1% and log 10 ‐transformed K s (Log 10 K s) increased by 11.0 and 5.8% from October 2007 to March 2008 and from October 2008 to March 2009, respectively; bulk density increased by 2.1% and Log 10 K s decreased by 4.9% from March to June in 2007; from June to October in 2007, bulk density decreased by 1.3% while a slight increase (1.4%) in Log 10 K s was observed. Both landscape and time significantly influenced ρ b and K s, and K s was more susceptible to temporal change than ρ b . Spatial patterns of ρ b and K s did not change significantly with time. Saturated hydraulic conductivity measurements taken in different seasons can affect runoff simulation results, and K s data measured in spring may result in underestimation of runoff in a rainy season.
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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 | 0.000 | 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) | 0.000 | 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".