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Quantitative Support for Convergence of Intrinsic Energies from Applied Magnetic Fields and “Noise” Fluctuations of Newton’s Gravitational Value within the Human Brain

2014· article· en· W1759314973 on OpenAlexaff
Michael A. Persinger, Kevin S. Saroka

Bibliographic record

VenueInternational Letters of Chemistry Physics and Astronomy · 2014
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPhysicsIonosphereMagnetic fieldComputational physicsQUIETHuman brainSchumann resonancesNoise (video)GravitationGravitational fieldNuclear magnetic resonanceClassical mechanicsGeophysicsNeuroscienceQuantum mechanicsPsychology

Abstract

fetched live from OpenAlex

When the energy within the mass of the human brain that is associated with the intrinsic range in fluctuations of the Gravitational Constant (G) is set equal to the energy from a magnetic field (B) within the cerebral volume and solved for B, a value in the order of 20 to 50 nT is obtained. Quantitative Electroencephalographic (QEEG) and sLORETA (Low Resolution Electromagnetic Tomography) analyses of cerebral cortical activity during exposure to a range of applied rotating, frequency-modulated, transcerebral (between the two temporal lobes) magnetic fields between < 1 nT and 7000 nT while volunteers sat within a darkened, quiet chamber were completed. There was marked enhancement of power within the 4 Hz to 10 Hz band within the right caudal (cuneus) hemisphere while the ~5 to 20 nT averaged strength magnetic fields were applied but no significant responses at lesser or greater intensities. These results suggest that a physical process coupled to the source of the fluctuation (~10-15 m3·kg-1·s-2) in G may interact with right hemispheric activity within the range at which gravity waves have been estimated to interact with Schumann frequencies generated between the earth and ionosphere.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.280
Teacher spread0.269 · 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 designBench or experimental
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

Citations6
Published2014
Admission routes1
Has abstractyes

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