The Influence of Magnetic Fields, Including the Planetary Magnetic Field, on Complex Life Forms: How Do Biological Systems Function in This Field and in Electromagnetic Fields?
Bibliographic record
Abstract
Life on Earth evolved to accommodate the biochemical and biophysical boundary conditions of the planet millions of years ago. The former includes nutrients, water, and the ability to synthesize other needed chemicals. The latter includes the 1 g gravity of the planet, radiation, and the geomagnetic field (GMF) of the planet. How complex life forms have accommodated the GMF is not known in detail, considering that Homo sapiens evolved a neurological system, a neuromuscular system, and a cardiovascular system that developed electromagnetic fields as part of their functioning. Therefore, all of these could be impacted by magnetic fields. In addition, many proteins and physiologic processes utilize iron ions, which exhibit magnetic properties. Thus, complex organisms, such as humans, generate magnetic fields, contain significant quantities of iron ions, and respond to exogenous static and electromagnetic fields. Given the current body of literature, it remains somewhat unclear if Homo sapiens use exogenous magnetic fields to regulate function and what can happen if the boundary condition of the GMF no longer exerts an effect. Proposed deep space flights to destinations such as Mars will provide some insights, as space flight could not have been anticipated by evolution. The results of such space flight “experiments” will provide new insights into the role of magnetic fields on human functioning. This review will discuss the literature regarding the involvement of magnetic fields in various normal and disturbed processes in humans while on Earth and then further discuss potential outcomes when the GMF is no longer present to impact host systems, as well as the limitations in the current knowledge. The GMF has been present throughout evolution, but many details of its role in human functioning remain to be elucidated, and how humans have adapted to such fields in order to develop and retain function remains to be elucidated. Why this understudied area has not received the attention required to elucidate the critical information remains a conundrum for both health professionals and those embarking on space flight. However, proposed deep space flights to destinations such as Mars may provide the environments to test and assess the potential roles of magnetic fields in human functioning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".