The spatial heterogeneity of riverbed saturated permeability coefficient in the lower reaches of the Heihe River Basin, Northwest China
Bibliographic record
Abstract
Abstract In arid region, direct infiltration from rainfall contributes little to groundwater compared with localized recharge from streams. How to quantify riverbed infiltration to groundwater systems is an important area of research in hydrology. In this study, saturated permeability coefficient of a riverbed in an arid inland river basin located in the northwest of China was obtained by Guelph Permeameter and laboratory analysis methods. The characteristics of riverbed infiltration and its spatial patterns were analysed using geostatistical method and kriging method. The results showed that the saturated permeability coefficient varied from 0.089 to 2.802 m/d, indicating moderate degree of variability. The Guelph Permeameter and laboratory test methods provided consistent estimates of saturated permeability coefficient. There was a strong spatial correlation for K fs of the riverbed in this study area when Range ( A ) was less than 0.276°, suggesting that the maximum sampling distance for saturated permeability coefficient of the riverbed was 0.276° under isotropic conditions. The K fs near the centre of the riverbed was higher than the value near riverbank. The K fs values decreased in the direction of upstream to downstream in the Heihe River Basin. The riverbed mechanical composition, initial soil water content and bulk density have significant influence up on the riverbed infiltration. Besides, the topographical factors including the width, altitude and distance factors of the riverbed together impacted the riverbed infiltration and the slope of the riverbed and also influenced the riverbed infiltration. Copyright © 2015 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".