Cumulative Residual Photon Power Density of ~10<sup>-12</sup> W•m<sup>-2</sup> During Mild “Distress” in the Same Space: Implications for Temporal Entanglement
Bibliographic record
Abstract
The potential for entanglement of photons generated within the space-time continuum to remain as residuals of photon flux density within the same space requires excess correlations between successive temporal increments. Our model predicted that the quantitative relationship with the fundamental quantity of 10 -20 J multiplied by the inverse diffusivity from the wave impedance and magnetic susceptibility of space and the electron orbital frequency would reflect excess correlation. The value would be ~10 -12 W• m -2 . To test this prediction experimentally, different mice were serially exposed within the same container box or each mouse was placed in different container boxes placed in the same space for 3 min per mouse while photons were measured from the dorsal surfaces in hyper-dark settings. Before asymptote was evident around 30 to 35 min of serial exposures the net increase in photon flux densities within that same space was 10 -12 W•m -2 . These results suggest that the same “space” may “store” photon-related information as indicated by previous experiments involving chemiluminescent reactions. We postulate that entanglement between photons emitted from biological systems during distress within the same space and specific concurrent magnetic field patterns may create the conditions for the “retrieval” of these photon patterns at some later date when these fields recur.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".