Local Stellar Kinematics from RAVE Data: IV. Solar Neighbourhood Age–Metallicity Relation
Bibliographic record
Abstract
Abstract We investigated the age–metallicity relation using a sample of 5691 F- and G-type dwarfs from RAdial Velocity Experiment Data Release 3 (RAVE DR3) by applying several constraints. (i) We selected stars with surface gravities log g(cm s−2) ≥ 3.8 and effective temperatures in the $5310\le T_{\text{eff}}\text{(K)}\le 7300$ range and obtained a dwarf sample. (ii) We plotted the dwarfs in metallicity sub-samples in the $T_{\text{eff}}\text{--}(J-K_s)_0$ plane to compare with the corresponding data of González Hernández & Bonifacio (2009) and identified the ones in agreement. (iii) We fitted the reduced dwarf sample obtained from constraints (i) and (ii) to the Padova isochrones and re-identified those which occupy the plane defined by isochrones with ages t ≤ 13 Gyr. (iv) Finally, we omitted dwarfs with total velocity errors larger than 10.63 km s−1. We estimated the ages using the Bayesian procedure of Jørgensen & Lindegren (2005). The largest age–metallicity slope was found for early F-type dwarfs. We found steeper slopes when we plotted the data as a function of spectral type rather than Galactic population. We noticed a substantial scatter in metallicity distribution at all ages. The metal-rich old dwarfs turned out to be G-type stars which can be interpreted as they migrated from the inner disc or bulge.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".