Capabilities of 3‐D wavelet transforms to detect plume‐like structures from seismic tomography
Bibliographic record
Abstract
The wavelet transform methods have been applied to viewing 3‐D seismic tomography by casting the transformed quantities into two proxy distributions, E‐max, the maximum of the magnitude of the local spectra about a local point and the associated local wavenumber, k‐max. Using a stochastic background noise, we test the capability of this procedure in picking up the coherent structures of upper‐mantle plumes. Plumes with a Gaussian shape and a characteristic width up to 2250 km have been tested for various amounts of the signal‐to‐noise ratios (SNR). We have found that plumes can be picked out for SNR as low as 0.08 db and that the optimal plume width for detection is around 1500 km. For plume width ranging between 700 km and 2000 km, the SNR can be lower than 1 db. This length‐scale falls within the range for plume‐detection based on the signal‐to‐noise levels associated with the current global tomographical models.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".