Using TDR to Estimate Hydraulic Conductivity and Air Entry in Growing Media and Sand
Bibliographic record
Abstract
Gas relative diffusivity measurements are key indicators of the quality of growing media. Previous studies have shown that this property can be estimated indirectly from measurements of the point of air entry (ψ a ), air‐filled porosity (θ a ), and saturated hydraulic conductivity ( K s ). Different tools are required to measure these parameters and this paper investigates how a single tool, time domain reflectrometry (TDR), already used to determine θ a from measurements of volumetric water content (θ), could be utilized to measure ψ a and K s in growing media. Cylinders were filled with 13 different substrates and coarse sand. A transient‐state technique (vertical infiltration at −1 hPa of water potential) was used to calculate K s from θ measurements in time. In growing media, calculated K s values from transient‐state experiment were statistically equal to estimates obtained from steady‐state measurements at a potential of −1hPa. However, both methods underestimated the K s values obtained under steady‐state conditions after a pulse of water had been applied or after prolonged wetting. For sand, TDR‐based measurements and steady‐state infiltration at −1 hPa, provided estimates of K s equal to those obtained after prolonged saturation. To estimate ψ a , TDR probes in a horizontal and a vertical position were tested in addition to a pressure transducer technique. For growing media, the horizontal positioning of the probes provided more consistent estimates of ψ a than the other two techniques. Estimates of ψ a with TDR in sand, both in vertical and horizontal position, were similar.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".