MétaCan
Menu
Back to cohort
Record W2970718617 · doi:10.4236/ojbiphy.2019.94018

Seed Germination and Their Photon Emission Profile Following Exposure to a Rotating Magnetic Field

2019· article· en· W2970718617 on OpenAlexaff
Victoria L. Hossack, Michael A. Persinger, Blake T. Dotta

Bibliographic record

VenueOpen Journal of Biophysics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsLaurentian University
Fundersnot available
KeywordsGerminationSunflowerMagnetic fieldRotating magnetic fieldPhotonHelianthusHorticultureBiologyEnvironmental scienceAgronomyPhysicsOptics

Abstract

fetched live from OpenAlex

A multitude of experiments have applied magnetic fields to plants or seeds and found a variety of different and sometimes contradicting results. A magnetic field generating device called the Chrysalis resonator has been shown to influence the brain activity of human participants, the photon emissions from bacteria, mammalian cell cultures and water. In this experiment sunflower seeds (Helianthus annus) were allowed to begin germination and then exposed to either the field generated by the Chrysalis resonator or a sham condition. Their growth and photon emissions were taken over the next 5 days. It was found that the seeds showed less germination 48 hours after exposure and significantly higher photon emissions when 3 seeds were measured together in a dish, but not if 2 seeds or 1 seed were measured. There were no significant differences in the photon measurements from the water the seeds were germinating in. These results may indicate that the seeds became more sensitive to the presence of neighbouring seeds. The photon emissions results were also significantly impacted by external weather conditions.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.237
Teacher spread0.233 · 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 designBench or experimental
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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueOpen Journal of BiophysicsSame topicMagnetic and Electromagnetic EffectsFrench-language works237,207