MAPPING LATERAL CHANGES IN CONDUCTANCE OF A THIN SHEET BY INVERTING TIME DOMAIN INDUCTIVE ELECTROMAGNETIC DATA
Bibliographic record
Abstract
The laterally varying conductance of thin sheet models can be estimated by inverting time domain inductive electromagnetic data. The advantage is that it only requires off time data and the result is independent of the transmitter location, the waveform, and the delay time. The inversion requires solving a simple, linear regularized least-squares problem with input values of dHzs/dz, Hys, Hxs and dHz/dt. The measured vertical gradient has been used in our previous work, but we simplified the problem by assuming that the product of the horizontal fields with the corresponding horizontal derivatives of resistance were zero and hence that the sheet had a uniform conductance. Through forward modeling we show that removing these assumptions and using all the fields we get better results when the spatial gradient of the conductance is strong and the vertical magnetic field gradient and horizontal fields are comparable. A comparison of the simplified and full inversion in an in-loop survey collected overtop a dry tailings pond in Sudbury, Ontario, Canada revealed that there were small differences around large resistance contrasts. Overall, the full inversion is more reliable, but the simplified approach is recommended as it is simpler, and can be performed in the field if the survey is designed to minimize the horizontal magnetic fields and if caution is taken around large resistance contrasts.
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 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".