Transient effect of low-intensity magnetic field on human motor control
Bibliographic record
Abstract
There is no consensus with respect to how extremely low frequency (ELF) magnetic fields (MF) affect biological systems. However, this information is crucial to establishing new guidelines for: (i) the new design of electronic devices, (ii) working conditions of exposed workers (e.g. electric linepersons), and in a general manner (iii) policies for human risk management. This study evaluates the effect of a sinusoidal 50 Hz, 1000 microT MF centered at the level of the head on human postural tremor of the index finger, using the wavelet analysis method. In addition to the detection of transient events in tremor time series linked with MF, this method was used to evaluate the differences between MF "on" and "off" conditions and between real and sham exposure in a counterbalanced protocol. Results indicate that neither transient events nor "off-on" or "on-off" MF transition effects were present in the postural tremor time series. Surprisingly, an unexpected significant time dependent decrease in tremor average power was noted along the 20s recordings. Interestingly, this effect was significantly more pronounced in the presence of MF. These results suggest a relaxing effect of ELF MF on motor control resulting in an attenuation of postural tremor intensity.
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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 | 0.000 |
| 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".