The metal–poor end of the Spite plateau
Bibliographic record
Abstract
Abstract We present the largest sample available to date of lithium abundances in extremely metal poor (EMP) Halo dwarfs. Four T eff estimators are used, including IRFM and Hα wings fitting against 3D hydrodynamical synthetic profiles. Lithium abundances are computed by means of 1D and 3D-hydrodynamical NLTE computations. Below [Fe/H]~−3, a strong positive correlation of A(Li) with [Fe/H] appears, not influenced by the choice of the Teff estimator. A linear fit finds a slope of about 0.30 dex in A(Li) per dex in [Fe/H], significant to 2–3 σ, and consistent within 1 σ among all the T eff estimators. The scatter in A(Li) increases significantly below [Fe/H]~−3. Above, the plateau lies at 〈A(Li) 3D, NLTE 〉 = 2.199 ± 0.086. If the primordial A(Li) is the one derived from standard Big Bang Nucleosynthesis (BBN), it appears difficult to envision a single depletion phenomenon producing a thin, metallicity independent plateau above [Fe/H] = −2.8, and a highly scattered, metallicity dependent distribution below.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".