The detection threshold for extremely low frequency magnetic fields may be below 1000 nT‐Hz in mice
Bibliographic record
Abstract
Previous experiments with mice have shown that a repeated 1 h daily exposure to an ambient magnetic field shielded environment induces analgesia (anti-nociception). This shielding reduces ambient static and extremely low frequency magnetic fields (ELF-MF) by approximately 100 times for frequencies below 120 Hz. To determine the threshold of ELF-MF amplitude that would attenuate or abolish this effect, 30 and 120 Hz magnetic fields were introduced into the shielded environment at peak amplitudes of 25, 50, 100 and 500 nT. At 30 Hz, peak amplitudes of 50, 100, and 500 nT attenuated this effect in proportion to the amplitude magnitude. At 120 Hz, significant attenuation was observed at all amplitudes. Exposures at 10, 60, 100, and 240 Hz with peak amplitudes of 500, 300, 500, and 300 nT, respectively, also attenuated the induced analgesia. No exposure abolished this effect except perhaps at 120 Hz, 500 nT. If the peak amplitude frequency product was kept constant at 6000 nT-Hz for frequencies of 12.5, 25, 50, and 100 Hz, the extent of attenuation was constant, indicating that the detection mechanism is dependent on the nT-Hz product. A plot of effect versus the induced current metric nT-Hz suggests a threshold of ELF-MF detection in mice at or below 1000 nT-Hz.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".