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Record W2129490987

Simulated Effects of Sudden Increases in Electromagnetic Activity on Deviations in Random Electron Tunnelling Behaviour Associated with Cognitive Intention

2014· article· en· W2129490987 on OpenAlexaff
Joey M. Caswell, David A. E. Vares, Lyndon M. Juden-Kelly, Michael A. Persinger

Bibliographic record

VenueJournal of consciousness exploration & research · 2014
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsLaurentian University
Fundersnot available
KeywordsQuantum tunnellingElectronRange (aeronautics)PhysicsPoint (geometry)Affect (linguistics)Magnetic fieldPower (physics)Statistical physicsCondensed matter physicsQuantum mechanicsPsychologyMathematicsCommunicationMaterials science
DOInot available

Abstract

fetched live from OpenAlex

Reliable evidence from the Jahn-Dunne studies conducted over several decades indicated that human proximity can affect the dynamics of certain processes that strongly depend upon “random” processes. Random Event Generators (REG) operate through “random” electron tunneling through spaces that are within the same order of magnitude as synapses. If the mechanisms by which these human-machine interactions occur involve electromagnetic processes, then application of specific temporally patterned magnetic fields to the human volume should affect the strength of the deviation from “random” variations. Whole-body exposure to ~400 nT, complex-patterned magnetic fields based upon 3 ms point durations reversed the effects of normal “intention” upon the operation of REGs. The energies generated within the cerebral volume by that field if emitted as irradiative power were within the range of the mass equivalent of an electron at the level of p-n junction of the semiconductor. These results support the hypothesis that “intention” can be affected experimentally and the energies within the vicinity of the actual dynamic space (~1 µm 2 ) of the p-n junction of the REG match the extended power of the magnetic energy contained within the cerebrum.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.376
Teacher spread0.334 · 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 designObservational
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

Citations4
Published2014
Admission routes1
Has abstractyes

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