A porous‐matrix sensor to measure the matric potential of soil water in the field
Bibliographic record
Abstract
Summary The matric potential of soil water is probably the most useful assessment of soil water status. However, the water‐filled tensiometer (the benchmark instrument for measuring matric potential) typically only operates in the range 0 to −85 kPa. In this paper, we report the development of a porous‐matrix sensor to measure matric potential in the approximate range −50 to −300 kPa. The sensor uses a dielectric probe to measure the water content of a ceramic material with known water retention characteristics. The calculation of matric potential takes into account hysteresis through the application of an appropriate model to measured wetting and drying loops. It is important that this model uses closed, rather than open, scanning loops. The calibrated sensors were tested in the field and the output compared with data from water‐filled tensiometers and dielectric measurements of soil water content. These comparisons indicated that conventional tensiometers gave stable but false readings of matric potential when soil dried to matric potentials more negative than −80 kPa. The porous‐matrix sensors appeared to give reliable readings of matric potential in soil down to −300 kPa and also responded appropriately to repeated wetting and drying. This porous‐matrix sensor has considerable potential to help understand plant responses to drying soil.
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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 | 0.000 | 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".