TEM data denoising based on cluster analysis and locally weighted linear regression
Why this work is in the frame
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Bibliographic record
Abstract
Transient electromagnetic (TEM) field data typically contain noises. When the noises are not properly dealt with, it can lead to unreliable inversion results. TEM data typically have a high signal-to-noise ratio (SNR) in early times and the SNR deteriorate over time. We present a novel denoising algorithm specifically designed for TEM data. The denoising method uses locally weighted linear regression (LWLR) to predict noise-free TEM data for a given time channel using TEM data collected at adjacent time channels. To obtain a better prediction result, we employed the k-means cluster analysis to determine the optimal width for the Gaussian kernel used by the LWLR algorithm. Synthetic tests showed that our algorithm can effectively eliminate the added noise in the TEM data. We also used one-dimensional (1D) inversion to invert the noise-contaminated data and the denoised data. We found that the inversion constructed model agrees better with the true model compared to the constructed model of the noise-contaminated data.
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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 | 0.001 |
| 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) | 0.000 | 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 it