MCMC chains representing stellar halo fits for Lane et al. (2023) paper published in MNRAS
Bibliographic record
Abstract
Results for the paper entitled "The stellar mass of the Gaia-Sausage/Enceladus accretion remnant" published in MNRAS by Lane, Bovy, & Mackereth. The results are in the form of MCMC posterior chains, which are used to generate the results recorded in Table 1 of the manuscript. The process used to generate these data is outlined in section 2 (data & sample preparation), section 3 (Density modelling framework), and section 4 (Presentation of results) of the manuscript. The data are contained within a single gzipped .tar file, organized in two directories: gse/ containing the results for fits to the GS/E kinematic samples, and all/ containing the results for the fits to the whole halo sample. In the gse/ directory, there are three directories: eLz/, AD/, and JRLz/, containing the results for the fits to each of the respective kinematically defined subsamples. Within these directories, as well as within all/, are a final set of directories for each of the eight density profiles considered in the work. Within each of these directories are a single file named "samples.npy" which is a binary file (see this link for format information) containing a numpy array of shape (# of parameters, # of posterior samples). The number of posterior samples is the number of walkers (100) times the number of samples per walker (10,000), minus the number of burn-in steps per walker (1,000) = 900,000. The number of parameters can be inferred from the density profiles definitions in section 3 of the manuscript, and the ordering of the parameters is the same as in Table 1. The units of the parameters are as in Table 1, except for the parameters theta and phi which are scaled such that they are defined on the interval (0,1), representing the intervals (0,2pi) and (0,pi) respectively.
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 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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.138 | 0.054 |
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".