Quantifying the Eruptive Flux on Venus With VenSAR Informed by Observations From Earth and Io
Bibliographic record
Abstract
Abstract The current volcanic output of Venus is unknown. In the 2030s, the VenSAR (Venus Synthetic Aperture Radar) instrument onboard the European Space Agency's (ESA) EnVision mission will estimate the global volcanic mass flux by looking for new flows with radar imaging at resolutions of 10 or 30 m/pixel, which can be compared with the 1990s‐era Magellan data (100–300 m/pixel). Based on eruptions on Earth and Io, we make suggestions for measuring the Venusian global eruptive flux. We do not need to observe small eruptions with Eruption Magnitude (based on mass) <3 because (at least on Earth) they produce <10% of the aggregate erupted mass. Assuming that the size–frequency distribution of Earth lava flows and domes holds on Venus and is augmented to include flows 75% longer as predicted for the Venus surface, we find that all Eruption Magnitude ≥3 eruptions are detectable by VenSAR–VenSAR imaging and >80% by VenSAR–Magellan. However, only 80% of eruptions may produce a detectable change in radar backscatter based on our examination of 24 basaltic terrestrial lava flows from 2014 to 2023 from the ESA Sentinel‐1a/b satellites. From observed Earth basaltic flows, thickness will rarely be measured on Venus due to low vertical accuracy. If VenSAR images 20%–40% of the most active volcanoes (as planned), it could detect 79%–92% of the flux if the Eruption Magnitude–frequency distribution is similar to Earth and Io. A few eruptions could then be extrapolated to a global flux, but this is dependent on quantifying the largest eruption, so targeting the right volcanoes is critical.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".