Effect of gravel content on soil water retention characteristics and thermal capacity of sandy and silty soils
Bibliographic record
Abstract
Abstract The presence of gravel in soils modifies the porosity, pore connectivity and pore size distribution in the soil matrix as well as the soil matrix-gravel interfaces. The aim of the present study is to investigate the effect of relative volume of gravel in samples with gravel mass fractions of 5,10, 20 wt% and varying bulk densities (1.3, 1.45, 1.55, 1.60, 1.65 g cm –3 ) on (i) total porosity, field capacity, plant available water holding capacity, (ii) pore size distribution and (iii) thermal capacity of repacked sandy and silty soils. The focus of the study was to determine if laboratory measured soil water retention curves considering (i), (ii), and (iii) can be predicted by a gravel-based weighting factor, R v , considering comprehensive significance tests. The sand-gravel mixtures show a decrease in the volume fractions of macropores and wide cores pores with an increase in the gravel contents, while the silt-gravel mixtures show an opposite trend. The root mean square errors (RMSE) between measured and fitted volumetric water contents, θ, between 0.006 and 0.0352 and between 0.002 and 0.004 for R v -weighted volumetric water contents indicate that the van Genuchten-based Peters-Durner-Iden (PDI) model is appropriate for fitting. The soil water retention curves with mass gravel contents of up to 10 wt% for silt and 20 wt% for sand can be well predicted by weighting factors (relative volume of rock fragments) in the range between 0.045 and 0.058 for silt, and between 0.112 and 0.119 for sand. The results also indicate a decrease in the Rv-weighted saturated, c vsat , and dry, c vdry , thermal capacity with an increase in the gravel contents for both soils. Further investigations are needed to examine if and whether measured sand- and silt-gravel mixtures with mass gravel contents below 10 % or rather 20 % can be predicted with a weighting factor.
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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 | 0.000 |
| 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 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".