Temporal and spatial properties of some deep moonquake clusters
Bibliographic record
Abstract
Using the event search method of Bulow et al. (2005), we have found 503 new deep moonquakes among the eight largest (in terms of total number) nearside source regions, increasing the number of identified events for each cluster an average of 36% over the existing catalog. These new events provide an improved deep event catalog, with which we explore some temporal and spatial aspects of deep moonquakes. First, we examine the spectra of moonquake occurrence times at each deep source region, and observe known tidal periodicities, notably those at ∼27 days and 206 days. Application of spectral methods for the analyses of point processes (discrete events) allows us to resolve closely spaced tidal periods not previously seen in moonquake data. Second, we pick seismic phase arrival times from optimized stacks of events from each source region. We use these picks, along with published velocity models, to relocate the nine source regions. Source regions A1 and A18 are the best located, with 95% confidence bounds of less than ±5° in latitude and longitude, and consistent with estimates from different studies. The locations of source regions A8 and A9 are poorly constrained, with uncertainties in latitude of up to ±28° resulting from the absence of clear phase arrivals at station 15. Large trade‐offs exist between relocation estimates and choice of velocity model, and the lack of reliable seismic phase arrivals severely affects location error.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".