Bibliographic record
Abstract
Abstract Models of a dark radiation sector with a mass threshold (WZDR+) have proved to be an appealing alternative to ΛCDM. These models provide simple comparison models, grounded in well-understood particle physics and with limited additional parameters. In addition, they have shown relevance in easing existing cosmological tensions, specifically the H 0 tension and the S 8 tension. Recently, measurements of CMB lensing by the ACT collaboration have provided strong additional information on clustering at late times. Within ΛCDM, these results yield a high value of S 8 at odds with weak-lensing measurements. In this work, we study this in the context of WZDR+, and find a much wider range of allowed values of S 8 , and in particular much better agreement between data sets and an overall improvement of fit versus ΛCDM. We expand our analyses to include a wide set of data, including the ACT-DR6 lensing data, as well as primary CMB information from ACT-DR4 and SPT-3G, scale-dependent power spectra from DES and measurements of H 0 from SH0ES. We find that there is little to no tension in measurements of structure within the data sets, and the inferred value of S 8 is generally lower than that in ΛCDM. We find that the inclusion of DES generally favors a higher H 0 , but there is some direct tension between the high-ℓ multipole data and this result. Future data should clarify whether this is a statistical artifact, or a true incompatibility of these datasets within this model.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".