Theoretical development of the differential scattering decomposition for the 3D resistivity experiment
Bibliographic record
Abstract
ABSTRACT In any numerical solution of the DC resistivity experiment, care must be taken to deal with strong heterogeneity of electrical conductivity. In order to examine the importance of conductivity contrasts, we develop a scattering decomposition of the DC resistivity equation in the sparse differential domain as opposed to the traditional dense integral formulation of scattering‐type equations. We remove the singularity in the differential scattered series via separation of primary and secondary conductivity, thereby avoiding the need to address the singularity in a Green's function. The differential scattering series is observed to diverge for large conductivity contrasts and to converge for small contrasts. We derive a convergence criterion, in terms of matrix norms for the weak‐form finite‐volume equations, that accounts for both the magnitude and distribution of heterogeneity of electrical conductivity. We demonstrate the relationship between the differential scattering series and the Fréchet derivative of the electrical potential with respect to electrical conductivity, and we show how the development may be applied to the inverse problem. For linearization associated with the Fréchet derivative to be valid, the perturbation in electrical conductivity must be small as defined by the convergence of the scattered series. The differential scattering formulation also provides an efficient tool for gaining insight into charge accumulation across contrasts in electrical conductivity, and we present a derivation that equates accumulated surface charge density to the source of scattered potential.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".