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Record W1799824198

Article Demonstration of Excess Correlation in Non-Local Random Number Generators Sharing Circular, Changing Angular Velocity Magnetic Fields

2015· article· en· W1799824198 on OpenAlexaff
Lyndon M. Juden-Kelly, Blake T. Dotta, David A. E. Vares, Michael A. Persinger

Bibliographic record

VenueJournal of consciousness exploration & research · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum Mechanics and Applications
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPhysicsMagnetic fieldQuantum entanglementComputational physicsQuantum mechanicsQuantum
DOInot available

Abstract

fetched live from OpenAlex

To test if temporally-coupled diametric shifts in parity could be demonstrated for non-local distances between “random” events generated by electron tunnelling-based circuits, two REG (Random Event Generators) were each exposed within a circular array of solenoids separated by 10 m. Each circular array generated a patterned rotating magnetic field that has previously produced transient excess correlation and entanglement in photon reactions and alterations in pH in spring water. During a 30 min interval the REGs were exposed first to an accelerating group velocity embedded with a diminishing frequency/phase-modulated field (the primer) followed by a decelerating group velocity embedded with an increasing frequency/phase-modulated magnetic field (the effector). Only after exposures for about 4 min to the second (effector) condition that is known to manifest the effects of entanglement did the random numbers deviate significantly and by more than one standard deviation in an opposite direction to each other. The estimated increments of energy were between 10 -21 and 10 -20 J which is within the range of the energy derived from the universe’s total force per Planck’s voxel distributed over the distance of the hydrogen wavelength. These results indicate that excess correlation can be generated within “random”, quantum electronic processes whose spatial domains are similar to neuronal synapses at the macro-level by appropriate applications of weak, microTesla level, magnetic fields.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.335
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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