Effects of Varying Proportions of Glass on Reflectance Spectra of HED Polymict Breccias
Bibliographic record
Abstract
Some meteorites contain significant amounts of glass, which, in most cases, probably results from impact processes on parent bodies.. Yamato 82202 is an example of one of the unequilibrated eucrites that contains significant proportions of impact glass distributed as veins throughout the meteorite. In other cases, fragments of glass are distributed throughout polymict breccias. For example, the polymict eucrite EET 87509 contains rare angular fragments of devitrified glass. Proportions of glass in most of these meteorites and in lithic clasts within these meteorites may vary locally from small amounts (less than one percent) to much larger amounts (subequal proportions of glass and mineral material). For example, some fragments within the South African polymict eucrite Macibini contain approximately 50% glass. The presence of these variable proportions of meteorite glass confirm the increased recognition that impact processes played an important role in the histories of asteroidal bodies. This study attempts to quantify the effects of a glass component on reflectance spectra by analyzing in the laboratory mixtures of varying proportions of a well-characterized HED polymict breccia and glass derived by melting a bulk sample of that breccia.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".