Bibliographic record
Abstract
We model the energy depletion of light from a distant object with two components: loss in photon rates (number of photons) and loss per photon particle. We model the rate loss based on intergalactic extinction, which, unlike extinctions within our local group of Milky Way, LMC and SMC, has not been previously accounted for. Ignoring intergalactic extinction results in the overestimation of cosmic distances with standard distance moduli. Such extinction effect is more profound on large scales (Gpc) that we have only started to observe today. We then apply the tired light model for energy loss (redshift) per photon. These two model components are mathematically very simple, leading to a new equation between luminosity distance and redshift. The model performed equally well as ΛCDM with Pantheon + SN data while outperforming it with the deep-field galaxy angular size test. The main implications of the intergalactic extinction model are two-fold: it identifies a major bias in distance measurements based on standard candles; it is also a key missing piece for static universe models to offer as viable alternatives to ΛCDM. Thus, the model has further implications on the interpretation of cosmic redshift, Hubble’s law, the Hubble constant, Hubble tension, and universe history.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".