Three‐Dimensional Sensitivity Distribution and Sample Volume of Low‐Induction‐Number Electromagnetic‐Induction Instruments
Bibliographic record
Abstract
There is an ongoing effort to improve the understanding of the correlation of soil properties with apparent soil electrical conductivity as measured by low‐induction‐number electromagnetic‐induction (LIN FEM) instruments. At a minimum, the dimensions of LIN FEM instruments' sample volume, the spatial distribution of sensitivity within that volume, and implications for surveying and analyses must be clearly defined and discussed. Therefore, a series of numerical simulations was done in which a conductive perturbation was moved systematically through homogeneous soil to elucidate the three‐dimensional sample volume of LIN FEM instruments. For a small perturbation with electrical conductivity similar to that of the soil, instrument response is a measure of local sensitivity (LS). Our results indicate that LS depends strongly on the orientation of the instrument's transmitter and receiver coils and includes regions of both positive and negative LS. Integration of the absolute value of LS from highest to lowest was used to contour cumulative sensitivity (CS). The 90% CS contour was used to define the sample volume. For both horizontal and vertical coplanar coil orientations, the longest dimension of the sample volume was at the surface along the main instrument axis with a length of about four times the intercoil spacing (s) with maximum thicknesses of about 1 and 0.3 s, respectively. The imaged distribution of spatial sensitivity within the sample volume is highly complex and should be considered in conjunction with the expected scale of heterogeneity before the use and interpretation of LIN FEM for mapping and profiling.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".