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Record W264590876 · doi:10.1016/j.fob.2015.04.015

Inverse relationship between photon flux densities and nanotesla magnetic fields over cell aggregates: Quantitative evidence for energetic conservation

2015· article· en· W264590876 on OpenAlexaff
Michael A. Persinger, Blake T. Dotta, Lukasz M. Karbowski, Nirosha J. Murugan

Bibliographic record

VenueFEBS Open Bio · 2015
Typearticle
Languageen
FieldMedicine
TopicBiofield Effects and Biophysics
Canadian institutionsLaurentian University
FundersFederation of European Biochemical Societies
KeywordsEarth's magnetic fieldMagnetic fieldPhotonMagnetometerIntensity (physics)Flux (metallurgy)PhysicsInverseField (mathematics)IncubationChemistryAtomic physicsNuclear magnetic resonanceAnalytical Chemistry (journal)Computational physicsOpticsEnvironmental chemistryBiochemistry

Abstract

fetched live from OpenAlex

The quantitative relationship between local changes in magnetic fields and photon emissions within ∼2 mm of aggregates of 10(5)-10(6) cells was explored experimentally. The vertical component of the earth's magnetic field as measured by different magnetometers was ∼15 nT higher when plates of cells removed from incubation were measured compared to plates containing only medium. Additional experiments indicated an inverse relationship over the first ∼45 min between changes in photon counts (∼10(-12) W·m(-2)) following removal from incubation and similar changes in magnetic field intensity. Calculations indicated that the energy within the aqueous volume containing the cells was equivalent for that associated with the flux densities of the magnetic fields and the photon emissions. For every approximately 1 nT increase in magnetic field intensity value there was a decrease of ∼2 photons (equivalent of 10(-18) J). These results complement correlation studies and suggest there may be a conservation of energy between expression as magnetic fields that are subtracted or added to the adjacent geomagnetic field and reciprocal changes in photon emissions when aggregates of cells within a specific volume of medium (water) adapt to new environments.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.194
GPT teacher head0.366
Teacher spread0.171 · 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 designObservational
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

Citations13
Published2015
Admission routes1
Has abstractyes

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