Inversion of Multiconfiguration Electromagnetic (DUALEM‐421) Profiling Data Using a One‐Dimensional Laterally Constrained Algorithm
Bibliographic record
Abstract
The collection of apparent electrical conductivity (σ a ) data from electromagnetic (EM) instruments has been used widely to map the spatial variation of average soil properties. Soil consists of horizons, however, and often the vertical change in properties can be an impediment to agricultural productivity or land use. A commonly used approach to discern changes with depth is the use of EM inversion techniques, but large amounts of data are still required. Conventionally this has meant that multiple passes are made at different heights with various instruments. Technological advances have seen the development of the DUALEM‐421 (Dualem Inc., Milton, ON, Canada), however, which is designed to collect σ a at multiple coil spacing and orientations simultaneously. What is now required is an inversion technique. We have developed the DUALEM‐2D algorithm, which consists of a one‐dimensional inversion with two‐dimensional smoothness constraints between adjacent one‐dimensional models. Calculations are based on cumulative response functions. The algorithm was evaluated using data generated from three synthetic models. Two practical examples, using σ a data acquired with a DUALEM‐421 for environmental studies, were used to evaluate the practical usefulness of the algorithm. The general patterns of the inverted models were shown to compare favorably with the available information and existing knowledge at each site.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".