Impact‐induced convection as the main mechanism for formation of lunar mare basalts
Bibliographic record
Abstract
Using a suite of numerical models, we show that impact‐induced convection in the Moon can explain the formation of lunar mare basalts, the depth range of their source region, the observed delay between impact basin formation and starting time of mare flows, and the long duration of the basaltic flows. The effects of an impact on the thermal evolution of the Moon and melt production in the mantle are investigated using convection calculations in an axisymmetric cylindrical coordinate system. An ascending mantle plume is allowed to melt as it crosses the depth of the solidus temperature. We consider two different models: permeable and impermeable. Five different viscosity models and three different impact basin sizes are examined. The total amount of melt produced by the permeable model with 1000 times viscosity contrast across the computation domain is comparable to the observed mare flows in Imbrium and Orientale basins. Moreover, the starting time of major melting in the mantle and its duration are also compatible with the observations. The model also allows a rigid lithosphere to develop beneath the basins that is capable of supporting the mascons largely created during the peak mare flow period. The model for South Pole Aitken basin results in a substantial amount of melting in the mantle, which does not seem to be compatible with the observations. A potassium, rare earth elements, and phosphorus (KREEP) layer with high concentration of radioactive elements incorporated directly beneath the crust has minor effects on the melt production in the mantle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".