Simulated Effects of Sudden Increases in Electromagnetic Activity on Deviations in Random Electron Tunnelling Behaviour Associated with Cognitive Intention
Bibliographic record
Abstract
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.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".