Optical Variability of Quasars with 20-Year Photometric Light Curves
Bibliographic record
Abstract
TotalDat.fits.gz: A FITS table storing information for each of the quasars used in the sample. The names, formats, and contents of each of the columns in this table are described in Table 1. All time-series data (MJD_x, MAG_x, MAG_ERR_x), structure function data (DT_REST_x, SF_x, SF_ERR_x), and PSD data (REST_FREQ_x, CARMA_PSD_x, CARMA_PSD_ERR_L_x, CARMA_PSD_ERR_U_x) are stored as arrays. EnsDat.fits.gz: A FITS table storing information for the ensemble analysis conducted on different subsets of the total sample. The names, formats, and contents of each of the columns in this table are described in Table 2. Similar to the previous file, time-series, structure function, and PSD data are stored as arrays. It should be noted that for each quasar/ensemble, each array will be the same length to conform to the FITS file standards. Therefore, to force each array to be the same shape, arrays shorter than the largest array will be filled with either NaNs or empty strings until they reach this maximum array length. There will be three columns for many of the values, one for each bandpass (g,r,i), also described in Table 1.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".