A QSO survey via optical variability and zero proper motion in the M 92 field
Bibliographic record
Abstract
We study a sample of 23 narrow-emission line galaxies (NELGs) which were selected by their strong variability as QSO candidates in the framework of a variability-and-proper motion QSO survey on digitised Schmidt plates. In previous work, we have shown that variability is an efficient method to find AGNs. The variability properties of the NELGs are however significantly different from those of the QSOs. The main aim of this paper is to clarify the nature of this variability and to estimate the fraction of AGN-dominated NELGs in this sample. New photometric and spectroscopic observations are presented, along with revised data from the photographic photometry. The originally measured high variability indices could not be confirmed. The diagnostic line-ratios of the NELG spectra are consistent with region-like spectra. No AGN could be proved, yet we cannot rule out the existence of faint low-luminosity AGNs masked by regions from intense star formation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".