Bibliographic record
Abstract
We have developed two techniques for simulating EM responses of a layered earth model; a general and an approximate method. The general method allows the computation of the magnetic field produced by systems with various current waveforms and survey configurations, including in-loop and out-of-loop for both moving and fixed transmitter with arbitrary location and orientation of receivers. The approximate method only allows the calculation of the vertical transient responses of the secondary currents during the off-time with receiver inside of the transmitter loop. Incorporating these two forward modelling techniques and both Marquardt and an Occam's inversion algorithm approaches, we have developed four methods to perform inverse modeling of transient electromagnetic soundings. A time domain conductivity-depth image (CDI) technique is also implemented. To prepare the data for this technique, an algorithm converting impulse response into step response has been developed. Armed with these inversion techniques, we can process ground and airborne data collected with systems using various current waveforms and survey configurations. The applications of these inversion techniques to synthetic layered-earth models demonstrate the effectiveness of these techniques. Interesting field data uses are also shown.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".