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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".