Calibration models for interpretation of soil salinity measurements using an electromagnetic induction technique
Bibliographic record
Abstract
A commercially available salinity sensor (model EM-38 of Geonics Ltd., Canada) has been developed for diagnosing and quantifying soil salinity in the field. There is a need, however, to calibrate the instrument reading against a more commonly used measure of soil salinity. Linear regression equations are reported that allow the interpretation of field instrument readings in terms of electrical conductivity of the saturation extract (ECe) measured in the laboratory. Instrument measurements and soil samples were taken at some 110 sites on various irrigation schemes distributed across South Africa. Samples were analysed in the laboratory and soils categorized according to texture, water status and salinity distribution with depth. Calibration equations were developed firstly by relating field instrument readings, taken either in the vertical or horizontal position, to a depth-integrated ECe value which was weighted for depth according to instrument response. Secondly, the mean of the readings taken in the vertical and horizontal positions at each site was related to the arithmetic mean ECe for the 0- to 1.2-m soil depth. A set of 12 linear regression equations was established in the first approach, and eight in the second. Two of the relationships showed statistical significance at the 5% level, the remainder at the 1% level. These equations enable the user of the EM-38 to derive a realistic index of soil salinity in terms of ECe, but calibration of the instrument for local soil conditions is preferable.
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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 | 0.001 |
| 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".