LITHIUM ION "CYCLOTRON RESONANCE" MAGNETIC FIELDS DECREASE SEIZURE ONSET TIMES IN LITHIUM-PILOCARPINE SEIZED RATS
Bibliographic record
Abstract
The cyclotron resonance equation predicts that the frequency of an applied magnetic field that might optimally interact with a single ion species may be computed as a function of the charge-to-mass ratio of the ion and the strength of the background static magnetic field. The present study was undertaken to discern the applicability of this equation for optimizing lithium ion utilization in the rat, as inferred by the predicted magnetic "ion resonance "field-induced shift of lithium's dose-dependent curve for seizure onset times (SOTs) when combined with the cholinergic agent pilocarpine. Groups of rats were administered 1.5 thru 3 mEq/kg lithium chloride (in 0.5 mEq/kg increments) and exposed to reference conditions or to one of three intensities (70 nanoTesla, 0.8 microTesla, or 25 microTesla) of a 85 Hz magnetic field calculated to resonate with lithium ions given the background static geomagnetic field of approximately 38,000 nanoTesla (0.38 Gauss). A statistically significant quadratic relationship for SOT as a function of magnetic field intensity (irrespective of lithium dose) was noted: this U-shaped function was characterized by equal SOTs for the reference and 25 microTesla groups, with a trend toward shorter SOTs for the 70 nanoTesla and 0.8 microTesla groups. Although not predicted by the equations, this report extends other findings suggestive of discrete intensity windows for which magnetic field frequencies derived from the cyclotron ion resonance equation may affect ion activity.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".