Quantitative Conditions and Convergent Operations That Are Compatible With the Universe as a Persistent Generator of Successive Simulations
Bibliographic record
Abstract
The capacity for computer-like simulations to be generated by massive information processing from electron-spin potentials supports Bostroms hypothesis that matter and human cognition might reflect simulations. Quantitative analyses of the basic assumptions indicate the universe may display properties of a simulation where photons behave as pixels and gravitons control the structural organization. The Lorentz solution for the square of the light and entanglement velocities converges with the duration of a single electron orbit that ultimately defines properties of matter. The approximately one trillion potential states within the same space with respect to the final epoch of the universe indicate that a different simulation, each with intrinsic properties, has been and will be generated as a type of tractrix defined by ±2 to 3 days (total duration 5 to 6 days). It may define the causal limits within a simulation. Because of the intrinsic role of photons as the pixel unit, phenomena within which flux densities are enhanced, such as human cognition (particularly dreaming) and the cerebral regions associated with those functions, create the conditions for entanglement or excess correlations between contiguous simulations. The consistent quantitative convergence of operations indicates potential validity for this approach. The emergent solutions offer alternative explanations for the limits of predictions for multivariate phenomena that could be coupled to more distal simulations.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".