Mechanisms of Lithification of Lunar Breccias
Bibliographic record
Abstract
Most lunar rocks studied to date are breccias of one form or another. The nature of the “glue” that bonds the fragments together and the processes by which an unconsolidated material becomes a non-porous, highly coherent breccia are poorly understood. Previous work on lunar impact melt rocks has investigated the relationships between the melt and cooler clasts. Textural studies have also been made on coarser grained breccias and melt rocks, which modeled temperatures, chemical changes and lithifi cation as a result of shock. Fine-grained friable lunar breccias have also been studied using a scanning electron microscope to determine mechanisms of lithifi cation. This study complements previous studies in that a range of lunar breccias, from impact melt to the very friable feldspathic breccia, have been examined from the Apollo missions 15, 16, and 17. In addition, samples of howardite and diogenite meteorites were used for comparison. Laboratory synthesized breccias were used as a control for shock conditions. There appears to be a strong link between the friability of a lunar breccia, the porosity and the amount of intergranular melt. As the amount of intergranular melt increases, the sample becomes more coherent and the porosity decreases. Since the intergranular melt is generated by the energy released by an impact event, there must be a correlation between the friability of the rock and its distance from the impact or its source energy of lithifi cation. This is complicated by the fact that on the moon, and other planetary bodies, it is likely that many impact events have affected the same rock. However, a sample should record the largest, closest and most recent event more strongly than other distant weaker, older events. It is thereby proposed that one can model the relative distance from the point of impact through the textural examination of a sample on the basis of intergranular melt, porosity, and friability.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".