Estimation of soil water content using electromagnetic induction sensors under different land uses
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The complex nature of podzolic soils makes investigating their subsurface challenging. Near-surface geophysical techniques, like electromagnetic induction (EMI), offer significant assistance in studying podzolic soils. Multi-coil (MC-EMI) and multi-frequency (MF-EMI) sensors were selected to maximize soil water content (SWC) prediction in this study. The objectives were to (i) compare apparent electrical conductivity (EC a ) measurements from the MC and MF-EMI sensors under different land use conditions, (ii) investigate the spatial variation of EC a , SWC, texture, soil organic matter (SOM), and bulk density (BD) under different land use conditions, and (iii) use statistical and geostatistical analysis to evaluate the effectiveness of EC a measurements in characterizing SWC under different land use conditions, considering the texture, SOM, and BD contents. The study found that MC-EMI had statistically significant relations (p-value < 0.05) with SWC relative to the MF-EMI. Multiple linear regression (MLR) models were also shown to be more effective in representing SWC variations (higher coefficient of determination and lower root mean square error) than simple linear regression models. MC-EMI sensor provided better SWC predictions compared to the MF-EMI sensor, possibly due to larger sampling depths differences between time domain reflectometry measured SWC (SWC TDR ) and MF-EMI sensor than those between SWC TDR and MC-EMI sensor. Lastly, cokriging of measured SWC was found to offer more accurate maps than cokriging of predicted SWC obtained from MLR across different land use conditions. The study has shown that EMI may not be highly effective for shallow depths, and EC a can be affected by various soil properties, making it difficult to extrapolate other parameters. However, EMI still shows promise as a reliable method for predicting SWC in boreal podzolic soils. Research into EMI’s usefulness for this purpose has yielded promising results, as indicated in this study. Further investigation is needed to fully harness the potential of this promising technique.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it