Distances of Quasars and Quasar‐like Galaxies: Further Evidence That Quasi‐stellar Objects May Be Ejected from Active Galaxies
Bibliographic record
Abstract
If high-redshift quasi-stellar objects (QSOs) are ejected from the nuclei of low-redshift galaxies, as some have claimed, a large portion of their redshift must be intrinsic (non-Doppler). If these intrinsic components have preferred values, redshifts will tend to cluster around these preferred values and produce peaks in the redshift distribution. Doppler ejection and Hubble flow components will broaden each peak. Because ejection velocities are randomly directed and Hubble flow components are always positive, in this model all peaks are expected to show an asymmetry, extending farther out in the red wing. If peaks are present showing this predicted asymmetry, it can lead directly to an estimate of quasar distances. Using two quasar samples, one with high redshifts and one with low, it is shown here that not only do all peaks in these two redshift distributions occur at previously predicted preferred values, but also they all show the predicted extra extension in the red wing. For the low- and high-redshift samples the mean cosmological components are found to be z c ~ 0.024 and ~0.066, respectively. The difference can be explained by the improved detection limit of the high-redshift sample. These results offer further evidence in favor of the model proposing that QSOs are ejected from active galaxies.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".