Multiple frequency compositing of spatially coincident GPR data sets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The compositing of spatially coincident GPR data sets acquired over a range of different antenna frequencies is a possible method for expanding the spectral bandwidth. A modelling study using the Berlage wavelet to represent the GPR source signature shows that the best results are obtained by applying an appropriate shift and weighting to the individual frequency components before compositing. It was also determined that application of only the weighting procedure significantly enhanced the composite signal. Compositing was performed on two spatially coincident multiple frequency data sets to examine its implementation with actual field data. A pre-compositing linear mute effectively suppressed the lower frequency direct wave interference with the very shallow reflections in higher frequency profiles. The best compositing results were obtained by using an optimal spectral whitening procedure to estimate the component weightings.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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