Potential Role of the Entanglement Velocity of 10<sup>23</sup> m·s<sup>-1</sup> to Accommodate Recent Measurements of Large Scale Structures of the Universe
Bibliographic record
Abstract
The aggregate of m7·s-1 from the product of the four geometric terms for increasing dimensions of a closed path (a circle) when set equal to the optimal combinations of the gravitational constant G and the universe’s mass, length and time results in a diffusivity term of 1023 m·s-1. Conversion of the total energy of the universe to volts per meter and Tesla results in a velocity of the same order of magnitude. The required f6 multiplication to balance the terms solves optimally for a frequency that when divided by the modified Planck’s value is the equivalent upper limit of the rest mass of a photon. Several experimental times associated with orbital distances for inertial frames are consistent with this velocity. Calculations indicate that during the final epoch the velocity from the energy derived from universal potential difference over length and magnetic fields will require only a unit frequency adjustment that corresponds to the energy equivalent of one orbit of a Bohr electron. We suggest that one intrinsic process by which large scale structures (Gigaparsec) are organized could involve this “entanglement velocity”. It would be correlated with the transformation of “virtual” or subthreshold values of the upper rest mass of photons to their energetic manifestation as the universe emerges from dark energy or matter that is yet to appear.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".