The realizable resistive limit: A new concept for mapping geological features spanning a broad range of conductances
Bibliographic record
Abstract
Abstract The integral of the step response from zero time to infinite time (the ideal resistive limit) can be used to determine the conductance of the ground, in theory, because the former is directly proportional to the latter. However, in a real time-domain airborne electromagnetic (AEM) system, it is impossible to measure the step response, or the ideal resistive limit. This is because (1) the off time is finite, being interrupted by the next transmitter pulse; (2) the total effect of all previous transmitter pulses is to reduce the measured response; and (3) the process of removing the primary field during the on time removes a component of the secondary response that has the same shape as the primary response. With a real time-domain AEM system, it is possible to estimate what is defined as the realizable resistive limit (RRL). The RRL can also be calculated theoretically for a horizontal thin sheet of known conductance. Hence, the measured data can be input into a nonlinear inversion scheme and used to estimate an apparent conductance. RRL is calculated using on-time data, which is above the noise level between 0.001 S and 100 000 S, so it is possible to map conductances in this eight-decade range. Traditional methods for deriving conductance use off-time data only and are restricted to a much smaller range of values (i.e., about two decades). A field example illustrates that, within the resistive areas, the RRL map shows many structural features and lithologies that are not evident on the map of conductance derived using off-time data. Within the conductive areas, the RRL image shows greater variation; a number of geologically meaningful features are also apparent. Another advantage of RRL images is that artifacts associated with current migration near the edge of conductive features are not as evident as they are in the off-time-derived conductance images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".