Several Ways to Calculate the Universal Gravitational Constant <i>G</i> Theoretically and Cubic Splines to Verify Its Measured Value
Bibliographic record
Abstract
In 1686, Newton discovered the laws of gravitation [1] and predicted the universal gravitational constant . In 1798, with a torsion balance, Cavendish [2] measured . Due to the low intensity of gravitation, it is difficult to obtain reliable results because they are disturbed by surrounding masses and environmental phenomena. Modern physics is unable to link G with other constants. However, in a 2019 article [3], with a new cosmological model, we showed that G seams related to other constants, and we obtained a theoretical value of . Here, we want to show that our theoretical value of G is the right one by interpreting measurements of G with the help of a new technique using cubic splines. We make the hypothesis that most G measurements are affected by an unknown systematic error which creates two main groups of data. We obtain a measured value of . Knowing that our theoretical value of G is in agreement with the measured value, we want to establish a direct link between G and as many other constants as possible to show, with 33 equations, that G is probably linked with most constants in the universe. These equations may be useful for astrophysicists who work in this domain. Since we have been able to link G with Hubble parameter H0 (which evolve since its reverse gives the apparent age of the universe), we deduce that G is likely not truly constant. It’s value probably slowly varies in time and space. However, at our location in the universe and for a relatively short period, this parameter may seem constant.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".