Apparent electrical conductivity mapping in managed podzols using multi-coil and multi-frequency EMI sensor measurements
Bibliographic record
Abstract
The research focused on utilizing apparent electrical conductivity (ECa) survey protocols in characterizing the spatial and temporal variability of soil physical and hydraulic properties in Western Newfoundland, Canada. In this study, two different non-invasive multi-coil and multi-frequency EMI sensors; CMD Mini-explorer and GEM-2, respectively were used to collect ECa data under different nutrient management systems at Pynn’s Brook Research Station, Pasadena. Results showed that due to the differences in investigation depths of the two EMI sensors, the linear regression models generated for SMC using the CMD Mini-explorer were statistically significant with the highest R² = 0.79 and the lowest RMSE = 0.015 m³ m⁻³ and not significant for GEM-2 with the lowest R² = 0.17 and RMSE = 0.045 m³ m⁻³. Furthermore, there is a significant relationship between the ECa mean relative differences (MRD) versus SMC MRD (R² = 0.33 to 0.70) for both multi-Coil and multi-Frequency sensors. In addition, the spatial variability of the ECa predicted soil properties are relatively consistent with lower variability compared to the measured soil properties. Conclusively, the ECa measurements obtained through either multi-coil or multi-frequency sensors have the potential to be successfully employed for soil physical and hydraulic properties at the field scale.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".