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Record W2548878051 · doi:10.4236/jbbs.2016.612043

Spatial-Temporal Quantitative Global Energy Differences between the Living and Dead Human Brain

2016· article· en· W2548878051 on OpenAlexaff
Nicolas Rouleau, Michael A. Persinger

Bibliographic record

VenueJournal of Behavioral and Brain Science · 2016
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsLaurentian University
Fundersnot available
KeywordsHuman brainCerebrumConsciousnessBrain deadPhysicsAstrophysicsNeurosciencePsychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

The dynamic differences between a living human brain and a clinically dead (fixed) human brain were measured over international Quantitative Electroencephalographic (QEEG) sites for 1 Hz increments between 1 Hz and 50 Hz. Although the expected greater power (μV2•Hz–1) for the living brain was apparent, the difference was particularly obvious for theta and low beta bands. The integrated square root values over the entire band indicated the difference in intrinsic charge-based energy between the living (higher) and dead brain was ~2•10–23 J. This quantity is remarkably proximal to the Cosmic Background Microwave value and would be consistent with the Hameroff-Penrose definition of consciousness that suggests a permeating presence derived from discrete physical events. A power value obtained by multiplying this increment of energy by the frequency of the universal hydrogen line resulted in ~10–12 W•m–2 when applied to the surface area of the human cerebrum. This value is the median flux density that has been measured from human brains during cognition and defines the ultra-weak photon emissions displayed by cells, tissue, and organisms. These results suggest that modern technology may now be sufficiently precise to discern the critical parameters that differentiate the living brain from the fixed “dead” brain. This information might be useful for future designs of virtual consciousness and simulations within cerebral space.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.367
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.091
GPT teacher head0.347
Teacher spread0.255 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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