Bibliographic record
Abstract
ABSTRACT We have shown that the varying physical constant model is consistent with the recently published variational approach wherein Einstein equations are modified to include the variation of the speed of light c, gravitational constant G, and cosmological constant Λ using the Einstein–Hilbert action. The general constraint resulting from satisfying the local conservation laws and contracted Bianchi identities provides the freedom to choose the form of the variation of the constants as well as how their variations are related. When we choose ${\dot{G}}/G = 3\,\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle\cdot}$}}{\dot{c}} /c,\,c = {c_0}\,{\rm{exp}}\,[({a^\alpha} - 1)],\,G = {G_0}\,{\rm{exp}}\,[3({a^\alpha} - 1)]$, and ${\rm{\Lambda }} = {{\rm{\Lambda }}_0}\ \exp [ {( {{a^{ - \alpha }} - 1} )} ]$, where a is the scale factor and α = 1.8, we are able to show that the resulting model: (a) fits the supernova 1a observational data marginally better than the Lambda cold dark matter (ΛCDM) model; (b) determines the first peak in the power spectrum of the cosmic microwave background temperature anisotropies at a multipole value of $l = 217.3$; (c) calculates the age of the Universe as 14.1 Gyr; and (d) finds the BAO acoustic scale to be 145.2 Mpc. These numbers are within less than 3 per cent of the values derived using the ΛCDM model. Surprisingly, we find that the dark-energy density is negative in a Universe that has significant negative curvature and whose expansion is accelerating at a faster rate than that predicted by the ΛCDM 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".