Multifrequency electromagnetic induction soil moisture characterization under different land uses in western Newfoundland
Bibliographic record
Abstract
Identifying and characterizing the spatial patterns in soil moisture variability under different land use conditions is crucial for agriculture, forestry, and civil and environmental engineering. Yet employing multifrequency (MF) electromagnetic induction (EMI) techniques to carry out this task is under-represented in boreal podzolic soils. This study ( i) compared four frequencies (∼2.8–80 kHz) for shallow mapping of soil moisture measured with a time–domain reflectometry at 0–20 cm soil depth under three different land use conditions (agricultural land, field road, and a recently cleared natural forest), ( ii) developed a relationship between apparent electrical conductivity (EC a ) measured using multifrequency EMI (GEM-2) and soil moisture, and ( iii) assessed the effectiveness of EC a as an auxiliary variable in predicting soil moisture variations under different land use conditions. The means of EC a measurements were calculated for the exact sampling location (ground truth data) in each land use condition at a research site, Pasadena, NL, Canada. Soil moisture–EC a linear regression models for the three land use conditions were only statistically significant for 38.3 kHz frequency and were further analyzed. Further statistical analysis revealed that EC a was primarily controlled by soil moisture for the three land use conditions, with the natural forest possessing the highest mean EC a and soil moisture. Geostatistical analysis revealed that cokriging EC a with less densely collected soil moisture improved the characterization accuracy of soil moisture variability across the different land use conditions. These results reveal the effectiveness of the georeferenced MF–EMI technique to rapidly assess intrafield soil moisture variability under different land uses.
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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 | 0.001 |
| 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".