Organic matter content influence on the hydraulic properties of sandy material
Bibliographic record
Abstract
Abstract The performance of the cover system, used in the reclamation of a mine site, is primarily assessed through hydraulic properties, including volumetric water content, suction, and saturated hydraulic conductivity ( k sat ). However, these properties may be influenced by factors such as soil mineralization (which refers to the process by which organic materials are converted into inorganic substances through natural processes), temperature (by the viscosity of water and the activity of microorganisms in the soil), and organic matter content (OMC), which complicate the accurate assessment of cover system performance. To better understand the impact of OMC on hydraulic properties, this study was initiated with two objectives: (i) evaluating the effect of OMC on the water retention curve (WRC) and k sat of a sandy material amended with peat and (ii) proposing equations to predict the WRC of sand amended with organic matter using the Fredlund and Xing model. This was accomplished through laboratory tests that determined the WRC and k sat of sand and sand amended with varying concentrations of peat (0%, 1%, 3%, 5%, 7.5%, 10%, 12.5%, and 15%). The investigation results indicate the air entry value (the suction at which the material begins to desaturate) evaluated using the sand mixture WRC did not show any notable variation. The k sat of the sand mixtures decreases with increasing peat concentration. In terms of prediction, the results obtained for the six mixtures tested in the laboratory showed an excellent agreement between predicted and experimental values, demonstrating the high accuracy with which the WRC s were predicted.
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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) | 0.001 | 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".