Incremental Shifts in pH Spring Water Can Be Stored as “Space-Memory”: Encoding and Retrieval Through the Application of the Same Rotating Magnetic Field
Bibliographic record
Abstract
Both four-dimensional (space-time) models and Casimir-like processes predict that the representations of stimulus-response pairing remain in altered or virtual states that can be potentially retrieved. Over a six month period we demonstrated “excess correlations” between mild acidification in quantities (50 ml) of spring water in a local space and the temporally contiguous incremental alkalinisation in nonlocal quantities of water when both loci were exposed to the same experimental paradigm that produced “entanglement” in photon reactions. The procedure required simultaneous exposures of both loci to specific patterns of rotating magnetic fields displaying specific rates of change in angular velocity. If the ~0.1 unit increases in pH within the non-local water samples due to injections of acetic acid in the local samples had been established on one day, comparable shifts occurred in the non-local water samples the following day when there were no injections of acetic acid if the space was exposed to the original magnetic field configurations. These results suggest that, like photon patterns, the “memory” or representation of pH (H+) shifts remain in space long after the stimulus has been removed and can be retrieved within that space if the specific electromagnetic field is repeated. NeuroQuantology | December 2013 | Volume 11 | Issue 4 | Page 511-518
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".