On signal contribution functions and sensitivity for frequency‐domain electromagnetic and direct current electrical resistivity methods
Bibliographic record
Abstract
Abstract Signal contribution functions can be integrated over space to calculate the response of an electrical or electromagnetic technique to a given resistivity distribution. On the other hand, sensitivity functions show how the measured signal changes with a change in resistivity in a region of the ground. Signal contribution functions and sensitivity have been previously presented for the direct current resistivity technique. While useful forms of both the signal contribution function and sensitivity are proportional to the correlation of the current densities from the normal and reciprocal configurations, it has not previously been shown how sensitivity can be derived directly from the signal contribution. For frequency‐domain electromagnetic techniques, there are existing expressions for sensitivity but not for the signal contribution. In this contribution, I show how the direct current signal contribution can be differentiated to obtain the sensitivity. I also derive an expression for the signal contribution function for frequency‐domain electromagnetics and show how it can be differentiated to obtain the sensitivity. The new signal contribution function has a term that, like the sensitivity, is proportional to the electrical current densities from the normal and reciprocal configurations and an additional term that is proportional to the correlation of the magnetic fields from the normal and reciprocal configurations. I show plots of these two terms and investigate their magnitudes as a function of the induction number. This new expression will be useful for testing numerical models and aids in understanding the measured results in frequency‐domain electromagnetics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".