Performance evaluation and calibration of capacitance sensor for estimating the salinity of reclaimed land
Bibliographic record
Abstract
Generally, soil moisture and salinity in reclaimed land are monitored using soil dielectric sensors such as time domain reflectometry, frequency domain reflectometry, and capacitance. The soil dielectric sensor measures apparent dielectric permittivity. However, apparent dielectric permittivity is affected by soil moisture, salinity, and texture. In this study, performance evaluation and calibration of a dielectric sensor (5TE; METER Group, Inc., Pullman, WA, USA) for monitoring soil salinity were performed. Laboratory calibration tests were completed, incorporating various levels of dry density, water content, and salinity. The soil salinity was determined by the electrical conductivity (EC)1:5 method. The volumetric water content as measured by the sensor was affected by dry density and water content. Generally, it linearly increased as dry density and water content increased. However, when dry density or water content was high, the measured value of the sensor increased nonlinearly. The bulk EC measured by sensor had no specific correlation with EC1:5. The EC1:5 measurement had a linear relationship with the gradient of θ and θs. Therefore, it can be estimated with a simple linear equation using θ from the soil test and θs from the capacitance sensor. The R2 value of the EC1:5 estimation equation was 0.98. The proposed equation requires θ from the gravimetric sample and θs from the sensor. Therefore, in the case of monitoring salinity using a sensor, it is recommended to measure the water content with a tensiometer. Keywords: salinity, capacitance sensor, volumetric water content, reclaimed land soil, saline soil DOI: 10.25165/j.ijabe.20201301.3905 Citation: Kim D, Son Y, Park J, Kim T, Jeon J. Performance evaluation and calibration of capacitance sensor for estimating the salinity of reclaimed land. Int J Agric & Biol Eng, 2020; 13(1): 206–210.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".