Density-dependent calibration of multisensor capacitance probes in coarse soil
Bibliographic record
Abstract
Zettl, J. D., Huang, M., Barbour, S. L. and Si, B. C. 2015. Density-dependent calibration of multisensor capacitance probes in coarse soil. Can. J. Soil Sci. 95: 331–336. Coarse-textured reconstructed soils are utilized extensively in the reclamation of mining waste. Accurate and continuous sensing of soil water content is required to understand soil water dynamics and evaluate the hydraulic characteristics of these soils. The EnviroSCAN (Sentek Pty. Ltd, Australia) is a semi-permanent multisensor capacitance probe (MCP) capable of continuous measurement of volumetric water content (θv) and has been used to monitor reclamation soil cover performance. Calibration of these probes is required to improve the accuracy of field measurements. In this study, field and laboratory measurements were undertaken over a range of water contents and bulk densities to refine the relationship between θv and scaled frequency (SF) measured by the MCP. The manufacturer's calibration equation tended to underestimate θv under wet conditions (θv>0.35 cm3 cm–3). Our experimental data showed that bulk density (ρb) did affect the MCP calibration and consequently a new calibration equation that includes the effect of ρb is developed using laboratory measurements and validated using field measurements. This equation provided the highest degree of correlation and the smallest standard deviation of prediction to measured values of θv for laboratory and field measurements, respectively. This calibration improves the application of the EnviroSCAN for coarse-textured soils such as those utilized in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".