Tension Infiltrometer Measurements: Implications of Pressure Head Offset due to Contact Sand
Bibliographic record
Abstract
The use of contact sand to achieve good hydraulic connection between the tension infiltrometer (TI) membrane and the soil is known to introduce an offset between the pressure head set on the bubble tower ( h 0 ) and the pressure head applied to the soil surface ( h s ). The nature and importance of the offset are poorly understood, however. Hence, the objectives of this study were to characterize the offset and to demonstrate its impacts on TI determinations of near‐saturated hydraulic conductivity, K ( h ), sorptive number, α*( h ), flow‐weighted mean pore diameter, D ( h ), and number of flow‐weighted mean pores per unit area, N ( h ). The offset, Δ h = h s − h 0 , consists of a constant elevation component and a variable head‐loss component. The elevation component increases h s relative to h 0 , and comprises most of the offset for low TI flux density, q ( h 0 ), and large contact sand hydraulic conductivity, K cs The head‐loss component decreases h s relative to h 0 , and becomes more important as q ( h 0 ) increases or K cs decreases. The offset has little effect on the accuracy of K ( h ), α*( h ), D ( h ), and N ( h ) when these relationships are insensitive to changes in h 0 When the relationships are sensitive to changing h 0 , the offset can change the shapes of the relationships; cause systematic overestimates of the K ( h ), α*( h ), and D ( h ) values; and cause systematic underestimates of the N ( h ) values. The amount of overestimate and underestimate increases with increasing offset and should be corrected using a form of Darcy's law to prevent the introduction of systematic biases in TI results.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".