Cerebral Networks of Interfacial Water: Analogues of the Neural Correlates of Consciousness in a Synthetic Three-Shell Realistic Head Model
Bibliographic record
Abstract
The physical properties of water, particularly the nature of interfacial water and pH shifts associated with dynamics of the hydronium ion near any surface, may be a primary source of the complex electromagnetic patterns frequently correlated with consciousness. Effectively all of the major correlates of consciousness, including the 40 Hz and 8 Hz coupling between the cerebral cortices and hippocampal formation, can be accommodated by the properties of water within a specific-shaped volume exposed to a magnetic field. In the present study, quantitative electroencephalographic activity was measured from an experimental simulation of the human head constructed using conductive dough whose pH could be changed systematically. Spectral analyses of electrical potentials generated over the regions equivalent to the left and right temporal lobes in humans exhibited patterns characteristic of Schumann Resonance. This fundamental and its harmonics are generated within the earth-ionospheric cavity with intensities similar to the volumetric intracerebral magnetic (~2 pT) and electric field (~6 × 10-1 V·m-1) strengths. The power densities for specific pH values were moderately correlated with those obtained from normal human brains for the fundamental (first) and second harmonic for the level simulating the cerebral cortices. Calculations indicated that the effective pH would be similar to that encountered within a single layer of protons near the plasma membrane surface. These results reiterate recent measurements in a large population of human brains showing the superimposition of Schumann power densities in QEEG data and indicate that intrinsic features of proton densities within cerebral water may be a fundamental basis to consciousness that can be simulated experimentally.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".