Transient Audio-Magnetotelluric Imaging Of A Buried Valley
Bibliographic record
Abstract
Thunderstorm activity produces large amounts of electromagnetic energy which is trapped within the earth-ionosphere waveguide. The random sum of energy from activity on a near global scale produces a low-level quasi-continuous source field. Very large, or equivalently, relatively nearby lightning discharges produce individual transient events whose amplitude are significantly larger than that of the low-level background field. Therefore, the best possible signal-to-noise ratio is realized by recording exclusively sources of a transient nature. However, the transient events are strongly linearly polarized, the polarization diversity of which can affect the estimation of earth response curves. It has been shown that an adaptive time domain averaging of the transient waveforms results in earth response curves whose bias converges to zero super-exponentially in stacked signal-to-noise ratio (Goldak et al., 2001). The efficacy of the algorithm is shown in the results of a transient audio-magnetotelluric (TAMT) survey conducted over a buried valley system in southern Manitoba, Canada. Twenty three sites at 200 m spacing were collected with the impedance tensor ˜Z and the magnetic field tipper ˜T estimated over the bandwidth 8 Hz - 32 kHz. The results of the TAMT survey agree very well with those of a time domain electromagnetic (TEM) survey conducted by the Saskatchewan Research Council with a Geonics EM-47 over nearly the same profile. Two dimensional OCCAM inversion of the TAMT data reveal the buried valley to be approximately 1 km wide, 70 m deep with a resistivity of approximately 12 ¡ m, incised into Cretaceous sediments of approximately 4 ¡ m resistivity.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".