Focused 70-cm Wavelength Radar Mapping of the Moon
Bibliographic record
Abstract
We describe new 70-cm wavelength radar images of the lunar near-side and limb regions obtained via a synthetic-aperture-radar patch-focusing reduction technique. The data are obtained by transmitting a circularly polarized pulsed waveform from the Arecibo telescope in Puerto Rico and receiving the echo in both senses of circular polarization with the Robert C. Byrd Green Bank Telescope in West Virginia. The resultant images in both polarizations have a spatial resolution as fine as 320 m 450 m near the lunar limb. The patch-focusing technique is a computationally efficient method for compensating for range migration and Doppler (azimuth) smearing over long coherence times, i.e., 983 s, which is needed to achieve the required Doppler resolution. Three to nine looks are averaged for speckle reduction and to improve the signal-to-noise ratio. At this long wavelength, the radar signal penetrates up to several tens of meters into the dry lunar surface materials, thus revealing details of the bulk loss properties and decimeter-scale rock abundance not evident in multispectral and other remote-sensing data. Application of the new radar images to the analysis of basalt flow complexes in Mare Serenitatis shows that the long-wavelength radar data are sensitive to differences in both flow age and composition, and may be particularly useful for studies of smaller deposits that do not have robust crater statistics. The new 70-cm lunar radar data are archived at the National Aeronautics and Space Administration Planetary Data System.
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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 | 0.000 |
| 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 | 0.001 |
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 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".