MétaCan
Menu
Back to cohort
Record W2131240187 · doi:10.2466/pms.98.3.958-966

Geophysical Variables and Behavior: Xcix. Reductions in Numbers of Neurons within the Parasolitary Nucleus in Rats Exposed Perinatally to a Magnetic Pattern Designed to Imitate Geomagnetic Continuous Pulsations: Implications for Sudden Infant Death

2004· article· en· W2131240187 on OpenAlexaff
Matthew J. Dupont, Bruce E. McKay, G. H. Parker, Michael A. Persinger

Bibliographic record

VenuePerceptual and Motor Skills · 2004
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsLaurentian University
Fundersnot available
KeywordsEarth's magnetic fieldMagnetic fieldPhysicsNucleusArousalNuclear magnetic resonanceBiologyNeuroscienceQuantum mechanics

Abstract

fetched live from OpenAlex

Correlational analyses have shown a moderate strength association between the occurrence of continuous pulsations, a type of geomagnetic activity within the 0.2-Hz to 5-Hz range, and the occurrence of Sudden Infant Deaths. In the present study, rats were exposed continuously from two days before birth to seven days after birth to 0.5-Hz pulsed-square wave magnetic fields whose intensities were within either the nanoTesla or microTesla range. The magnetic fields were generated in either an east-west (E-W) or north-south (N-S) direction. At 21 days of age, the area of the parasolitary nucleus (but not the solitary nucleus) was significantly smaller, and the numbers of neurons were significantly less in rats that had been exposed to the nanoT fields generated in the east-west direction or to the microTesla fields generated within either E-W or N-S direction relative to those exposed to the N-S nanoTesla fields. These results suggest nanoTesla magnetic fields, when applied in a specific direction, might interact with the local geomagnetic field to affect cell migration in structures within the brain stem that modulate vestibular-related arousal and respiratory or cardiovascular stability.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.276
Teacher spread0.252 · 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 teacher head, 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

Citations15
Published2004
Admission routes1
Has abstractyes

Explore more

Same venuePerceptual and Motor SkillsSame topicNeuroscience of respiration and sleepFrench-language works237,207