Bibliographic record
Abstract
The mounting evidence for anomalously large peculiar velocities in our Universe presents a challenge for the $\ensuremath{\Lambda}\mathrm{CDM}$ paradigm. The recent estimates of the large-scale bulk flow by Watkins et al. are inconsistent at the nearly $3\ensuremath{\sigma}$ level with $\ensuremath{\Lambda}\mathrm{CDM}$ predictions. Meanwhile, Lee and Komatsu have recently estimated that the occurrence of high-velocity merging systems such as the bullet cluster (1E0657-57) is unlikely at a $6.5--5.8\ensuremath{\sigma}$ level, with an estimated probability between $3.3\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}11}$ and $3.6\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}9}$ in $\ensuremath{\Lambda}\mathrm{CDM}$ cosmology. We show that these anomalies are alleviated in a broad class of infrared-modifed gravity theories, called brane-induced gravity, in which gravity becomes higher-dimensional at ultralarge distances. These theories include additional scalar forces that enhance gravitational attraction and therefore speed up structure formation at late times and on sufficiently large scales. The peculiar velocities are enhanced by 24--34% compared to standard gravity, with the maximal enhancement nearly consistent at the $2\ensuremath{\sigma}$ level with bulk flow observations. The occurrence of the bullet cluster in these theories is $\ensuremath{\approx}{10}^{4}$ times more probable than in $\ensuremath{\Lambda}\mathrm{CDM}$ cosmology.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".