Effects of lateral variations in megaregolith thickness on predicted lunar seismic signals
Bibliographic record
Abstract
Abstract We use a modified phonon synthetic seismogram method to investigate the effects of laterally varying megaregolith thickness on the propagation of seismic energy and on the resulting seismic signals recorded at various epicentral distances from the source. We show that receivers located in large impact structures, with thin crust and thinner megaregolith, can record seismic signals that are less affected by high levels of scattering. In particular, receivers located away from the basin edge by a distance greater than or equal to the thickness of the surrounding megaregolith can record seismograms in which secondary arrivals containing important information about interior structure can be more readily identified. Seismic sources located beneath the near‐surface scattering layer, such as deep lunar quakes, are also advantageous because the resulting seismograms are less affected by high levels of scattering than those from sources within the scattering layer or surface impacts.
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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 | 0.004 |
| 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) | 0.001 | 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".