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Record W4317885446 · doi:10.3389/fphy.2023.1026460

Hypomagnetic field effects as a potential avenue for testing the radical pair mechanism in biology

2023· article· en· W4317885446 on OpenAlexafffund
Hadi Zadeh‐Haghighi, Rishabh Rishabh, Christoph Simon

Bibliographic record

VenueFrontiers in Physics · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsMechanism (biology)Field (mathematics)Magnetic fieldPhysicsPerspective (graphical)Chemical physicsBiologyBiophysicsComputer scienceQuantum mechanicsMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Near-zero magnetic fields, called hypomagnetic fields, are known to impact biological phenomena, including developmental processes, the circadian system, neuronal and brain activities, DNA methylation, calcium balance in cells, and many more. However, the exact mechanism underlying such effects is still elusive, as the corresponding energies are far smaller than thermal energies. It is known that chemical reactions involving radical pairs can be magnetic field dependent at very low intensities comparable to or less than the geomagnetic field. Here, we review in detail hypomagnetic field effects from the perspective of the radical pair mechanism, pointing out that under certain conditions, they can be comparable or even stronger than the effects of increasing the magnetic field. We suggest that hypomagnetic field effects are an interesting avenue for testing the radical pair mechanism in biology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.008
GPT teacher head0.243
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2023
Admission routes2
Has abstractyes

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