Application of <scp>EMI</scp> ‐Measured Magnetic Susceptibility to Characterise Soil Drainage Conditions Over Various Soil Types
Bibliographic record
Abstract
ABSTRACT Electromagnetic induction (EMI), by Geonics EM38, was used to characterise the volumetric magnetic susceptibility (MS) of soils on 12 farms in southwestern Ontario, Canada. Three different points on lower, middle and upper slope positions were selected at each farm to represent poorly‐, moderate‐ and well‐drained soil. Soil core samples were collected for each measurement point, from which soil redoximorphic conditions (gleying and mottling) were characterised at 5 cm depth increments. The volume MS, mass‐specific MS and frequency dependence (FD) of MS of soil samples were carried out using Bartington MS2C and MS2B sensors, respectively. The impact of heating the samples to 400°C and 700°C on soil MS was also investigated. Results show that at each farm, the lowest volume MS values belong to soils at the lower slope position, which is poorly drained, and the highest volume MS values belong to the soils at the upper slope position, which is well drained. The inverted models from apparent MS data, measured by EM38, seem to be good representatives of volume MS readings attained from core samples. Results show that at most of the selected points, while the FD is higher in poorly drained points than in moderate and well‐drained ones, the mass‐specific MS shows an opposite behaviour, which can be used as attributes to characterise poorly and well‐drained soil conditions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".