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Record W2008578996 · doi:10.1080/00207450590523945

COMPLEX MAGNETIC FIELDS ENABLE STATIC MAGNETIC FIELD CUE USE FOR RATS IN RADIAL MAZE TASKS

2005· article· en· W2008578996 on OpenAlexaff
Bruce E. McKay, Michael A. Persinger

Bibliographic record

VenueInternational Journal of Neuroscience · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPsychologyRadial arm mazeMagnetostaticsNeuroscienceStimulus (psychology)Magnetic fieldAdult maleAudiologyWorking memoryCognitionPhysicsCognitive psychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Male Wistar rats were trained in an eight-arm radial maze task (two sessions per day, delayed-non-matching-to-sample) that included an intramaze static magnetic field "cue" (185 microT) specific to the entrance point of one of the arms. Rats were exposed daily for 60 min to a complex magnetic field waveform (theta-burst pattern, 200-500 nT), presented with several different interstimulus intervals (ISIs), either immediately following training sessions or immediately preceding testing sessions. Application of the theta-burst stimulus with a 4000 ms ISI significantly improved the rats' memory for the arm of the radial maze whose position was indicated by the presence of a static magnetic field cue. Reference memory errors were homogeneously distributed among all eight arms of the maze for sham-exposed rats, and among the other seven arms of the maze for complex magnetic field-treated rats. These results suggest that static magnetic field cues may be salient orienting cues even in a microenvironment such as a radial maze, but their use as a cue during maze learning in rats is dependent on whole-body application of a specific time-varying complex magnetic field.

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 score0.608
Threshold uncertainty score0.348

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 head0.023
GPT teacher head0.294
Teacher spread0.271 · 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

Citations9
Published2005
Admission routes1
Has abstractyes

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