Impact of density on the hydraulic properties of peat and the time domain reflectometry (TDR) moisture calibration curve
Bibliographic record
Abstract
The hydraulic properties of a peat used in a commercial peat biofilter were evaluated to determine their relationship with density and to establish a time domain reflectometry (TDR) calibration curve for water content as a function of the measured dielectric constant. The peat studied was a milled Sphagnum peat with a high organic content (99%). The dry densities evaluated in this study ranged from 90 to 180 kg/m3. The saturated hydraulic conductivity (Ks) decreased with an increase in dry density (ρdry) and was found to follow a log-linear relationship (Ks = 0.2462 exp(0.0438ρdry), correlation coefficient R2 = 0.9789). As expected, the soil moisture curve was impacted by density, with a higher density resulting in higher water contents for a given suction. The data were fit to the van Genuchten relationship. A TDR calibration curve was generated at five different densities. A comparison of the curves indicates that the water content as a function of dielectric constant was not dependent on density because of the significantly larger dielectric constant (Ka) of water compared with those of peat solids and air-filled voids. The TDR calibration curve for the peat evaluated in this study (volumetric water content Θv = 0.2667 ln(Ka) 0.1405, R2 = 0.9564) predicted higher water contents for a given dielectric constant compared with those from similar calibration curves for peat published in the literature. The data were compared with those from six other studies and indicated that the TDR calibration varied for different organic soils. The density-dependent hydraulic parameters and TDR calibration curve are important parameters needed to study the hydraulics of peat biofilters.Key words: peat, TDR, time domain reflectometry, density, hydraulics, soil moisture.
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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 | 0.001 | 0.004 |
| 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".