Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<i>Context.<i/> Since July 2014, the <i>Gaia<i/> mission has been engaged in a high-spatial-resolution, time-resolved, precise, accurate astrometric, and photometric survey of the entire sky.<i>Aims.<i/> We present the <i>Gaia<i/> Science Alerts project, which has been in operation since 1 June 2016. We describe the system which has been developed to enable the discovery and publication of transient photometric events as seen by <i>Gaia<i/>.<i>Methods.<i/> We outline the data handling, timings, and performances, and we describe the transient detection algorithms and filtering procedures needed to manage the high false alarm rate. We identify two classes of events: (1) sources which are new to <i>Gaia<i/> and (2) <i>Gaia<i/> sources which have undergone a significant brightening or fading. Validation of the <i>Gaia<i/> transit astrometry and photometry was performed, followed by testing of the source environment to minimise contamination from Solar System objects, bright stars, and fainter near-neighbours.<i>Results.<i/> We show that the <i>Gaia<i/> Science Alerts project suffers from very low contamination, that is there are very few false-positives. We find that the external completeness for supernovae, <i>C<i/><sub>E<sub/> = 0.46, is dominated by the <i>Gaia<i/> scanning law and the requirement of detections from both fields-of-view. Where we have two or more scans the internal completeness is <i>C<i/><sub>I<sub/> = 0.79 at 3 arcsec or larger from the centres of galaxies, but it drops closer in, especially within 1 arcsec.<i>Conclusions.<i/> The per-transit photometry for <i>Gaia<i/> transients is precise to 1% at <i>G<i/> = 13, and 3% at <i>G<i/> = 19. The per-transit astrometry is accurate to 55 mas when compared to <i>Gaia<i/> DR2. The <i>Gaia<i/> Science Alerts project is one of the most homogeneous and productive transient surveys in operation, and it is the only survey which covers the whole sky at high spatial resolution (subarcsecond), including the Galactic plane and bulge.
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.
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 | 0.000 | 0.000 |
| 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 it