ORIGINS OF THE THICK DISK AS TRACED BY THE ALPHA ELEMENTS OF METAL-POOR GIANT STARS SELECTED FROM RAVE
Bibliographic record
Abstract
Theories of thick-disk formation can be differentiated by measurements of stellar elemental abundances. We have undertaken a study of metal-poor stars selected from the RAVE spectroscopic survey of bright stars to establish whether or not there is a significant population of metal-poor thick-disk stars ([Fe/H] ≲ −1.0) and to measure their elemental abundances. In this Letter, we present abundances of four α-elements (Mg, Si, Ca, and Ti) and iron for a subsample of 212 red giant branch and 31 red clump/horizontal branch stars from this study. We find that the [α/Fe] ratios are enhanced, implying that enrichment proceeded by purely core-collapse supernovae. This requires that star formation in each star-forming region had a short duration. The relative lack of scatter in the [α/Fe] ratios implies good mixing in the interstellar medium prior to star formation. In addition, the ratios resemble that of the halo, indicating that the halo and thick disk share a similar massive star initial mass function. We conclude that the α-enhancement of the metal-poor thick disk implies that direct accretion of stars from dwarf galaxies similar to surviving dwarf galaxies today did not play a major role in the formation of the thick disk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".