‘Swiss-cheese’ inhomogeneous cosmology and the dark energy problem
Bibliographic record
Abstract
We study an exact Swiss-cheese model of the universe, where inhomogeneous LTB patches are embedded in a flat FLRW background, in order to see how observations of distant sources are affected. We focus mainly on the redshift, both perturbatively and non-perturbatively: the net effect given by one patch is suppressed by ( L / R H ) 3 (where L is the size of one patch and R H is the Hubble radius). We disentangle this effect from the Doppler term (which is much larger and has been used recently (Biswas et al 2007 J. Cosmol. Astropart. Phys. JCAP12(2007)017 [astro-ph/0606703]) to try to fit the SN curve without dark energy) by making contact with cosmological perturbation theory. Then, the correction to the angular distance is discussed analytically and estimated to be larger, , perturbatively and non-perturbatively (although it should go to zero after angular averaging).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".