Evaluation of Electromagnetic Induction to Characterize and Map Sodium‐Affected Soils in the Northern Great Plains
Bibliographic record
Abstract
Sodium‐affected soils (SAS) cover more than 10 million acres in the Northern Great Plains. Improving the classification, interpretation, and mapping of SAS is a major goal of the USDA‐NRCS as Northern Great Plains soil surveys are updated. Apparent electrical conductivity (EC a ) as measured with ground conductivity meters has shown promise for mapping SAS, but use of this geophysical tool needs additional evaluation. This study used an EM‐38 MK2‐2 meter (Geonics Limited, Mississauga, Ontario), a Trimble AgGPS 114 L‐band DGPS (Trimble, Sunnyvale, CA) and the RTmap38MK2 program (Geomar Software, Inc., Mississauga, Ontario) on an Allegro CX field computer (Juniper Systems, North Logan, UT) to collect, observe, and interpret EC a data in the field. The EC a map generated on‐site was then used to guide collection of soil samples for soil characterization and to evaluate the influence of soil properties in SAS on EC a as measured with the EM‐38MK2‐2. Stochastic models contained in the ESAP software package were used to estimate the sodium adsorption ratio (SAR) and salinity levels from the measured EC a data in 30 cm depth intervals to a depth of 90 cm and for the bulk soil (0–90 cm). This technique showed promise, with meaningful spatial patterns apparent in the EC a data. However, many of the stochastic models used for salinity and SAR for individual depth intervals and for the bulk soil had low R 2 values. At both sites, significant variability in soil clay and water contents along with a small number of soil samples taken to calibrate the EC a values to soil properties likely contributed to these low R 2 values.
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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 | 0.000 | 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".