Impact craters on regional plains on Venus: Age relations with wrinkle ridges and implications for the geological evolution of Venus
Bibliographic record
Abstract
A global assessment of impact craters on Venus ≥30 km in diameter shows that 146 craters are completely or partially superposed on the regional plains that dominate the surface and typically are deformed by wrinkle ridges. In this study, we found that 10 of these craters are deformed by wrinkle ridges (pre‐WR), 93 craters are superposed on wrinkle ridges (post‐WR), and 43 craters were found to be ambiguous cases. We compared our findings with the results of a similar study by McGill (2004) done for craters of all sizes but for ∼1/3 of the surface of Venus. We found that the percentage of pre‐WR craters found by McGill (2004) among the unambiguous cases (15%) is close to our percentage (10%), but we are in disagreement on the classification of a significant number of specific craters and in this analysis we describe the major reason for the disagreements. We then summarize published data on the crater densities for different plains on Venus. We assess Monte Carlo modeling of volcanic resurfacing, which competed with the ongoing formation of impact craters. On the basis of these data and analyses, we conclude that the mean age of the regional plains is close to the mean global surface age of Venus and that the potential time span of regional plains emplacement was relatively short. We interpret our results as evidence that wrinkle ridging on Venus was a distinct tectonic phase that occurred rather early in post‐regional‐plains time, probably within the first 10%. Although the exact duration remains uncertain, regional and global stratigraphic relationships strongly support the hypothesis that wrinkle ridging on Venus predominantly occurred during a specific phase of history. The geologic record of wrinkle ridging and post‐plains tectonic evolution of volcanic plains on the Moon and Mars show interesting similarities to those of Venus.
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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 | 0.001 |
| 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".