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
Summary A model of the Earth can be described using a Fourier basis represented by its wavenumber components. In full waveform inversion (FWI), our ultimate objective is to access all the available model wavenumbers in the recorded data that are not already accurately present in the initial velocity model. In inverting for these model wavenumbers, it is important to locate their imprint in the data. To achieve this, I review the relation between the model wavenumber buildup and the inversion process. Specifically, I emphasize a focus on the model wavenumber components and identify their individual influence on the data. Missing the energy for a single vertical low wavenumber from the residual model between the true Marmousi model and some initial linearly increasing velocity model produces a worse least-square fit to the data than the initial model it self where all the residual model wavenumbers are missing. This extreme realization demonstrates the importance of the low model wavenumbers in utilizing the higher wavenumbers, especially those attained in an order dictated by a scattering angle filter. A numerical Marmousi example demonstrates the important role that a scattering angle filter plays in managing the continuation process from low to high model wavenumbers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it