Introduction to this special section: Planetary geophysics
Bibliographic record
Abstract
While geophysical exploration of Earth is well established as a critical method for understanding planetary processes, many current and planned missions offer great opportunities for geophysicists to apply their skills and expertise to space exploration. Programs such as NASA's Artemis aiming to bring humans back to the moon and the James Webb Space Telescope for deep space imaging, the 10-year extension plan for the Chinese Lunar Exploration Program's Chang'e missions, the Japanese Aerospace Exploration Agency's Martian Moons eXploration program, and the European Space Agency's European Large Logistics Lander targeting the moon are just a few examples. NASA's Commercial Lunar Payload Services program proposes to send two commercial landers to the moon every year for the remainder of the decade. Several already-manifested payloads in the program involve geophysical instrumentation including heat flow probes, magnetotelluric sounding systems, and seismometers. Seismic data continue to arrive from NASA's InSight mission to Mars as the dusty solar panels still deliver a little energy, emphasizing that geophysics is and will remain an important tool for planetary exploration moving forward.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.101 | 0.088 |
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".