Lithospheric Structure of the Moon and Correlation with Deep Moonquake Source Regions
Bibliographic record
Abstract
Source Regions Pascal Audet and Catherine L. Johnson, University of California Berkeley (paudet@berkeley.edu), Department of Earth and Ocean Sciences, University of British Columbia, Vancouver (cjohnson@eos.ubc.ca), Planetary Science Institute, Tucson, AZ. Introduction: A major scientific result from the Apollo Passive Seismic Experiment (APSE) was the detection of several thousand moonquakes that occur with tidal periodicities and emanate from distinct source regions with depths of 700–1200 km [1]. These deep moonquakes (DMQ) have provided first order information on the interior deep structure of the Moon; however little is known about the actual source regions, except that they appear to be less than a few km in spatial extent [2]. Observed DMQ source regions are confined to the lunar nearside, due to the placement of the 4 APSE seismometers. However, as has been noted previously, it is clear that the deep source regions are not distributed randomly even within some nominal region of sensitivity about the centroid of the Apollo seismic network [3]. In particular, DMQ source regions generally occur in association with the major nearside basins and few are located below the nearside highlands, suggesting the influence of long-lived spatial variations in thermal and/or compositional structure [3]. Here we investigate relationships between the DMQ source region locations and lithospheric and crustal structure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".