Soil moisture monitoring at the field scale using neutron probe
Bibliographic record
Abstract
Measurement of the moisture variation in soils is required for geotechnical design and research because soil properties and behavior can vary as moisture content changes. The neutron probe, which was developed more than 40 years ago, is commonly used to monitor soil moisture variation in the field. This study reports a full-scale field monitoring of soil moisture using a neutron moisture probe for a period of more than 2 years in the Melbourne (Australia) region. On the basis of soil types available in the Melbourne region, 23 sites were chosen for moisture monitoring down to a depth of 1500 mm. The field calibration method was used to develop correlations relating the volumetric moisture content and neutron counts. Observed results showed that the deepest “wetting front” during the wet season was limited to the top 800 to 1000 mm of soil whilst the top soil layer down to about 550 mm responded almost immediately to the rainfall events. At greater depths (550 to 800 mm and below 800 mm), the moisture variations were relatively low and displayed predominantly periodic fluctuations. This periodic nature was captured with Fourier analysis to develop a cyclic moisture model on the basis of an analytical solution of a one-dimensional moisture flow equation for homogeneous soils. It is argued that the model developed can be used to predict the soil moisture variations as applicable to buried structures such as pipes.
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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".