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Record W4406827281

An Investigation of Solar Features, Test Environment, and Gender Related to Consciousness-Correlated Deviations in a Random Physical System

2014· article· en· W4406827281 on OpenAlexaff
Joey M. Caswell, Lyndon M. Juden-Kelly, David A. E. Vares, Michael A. Persinger

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languageen
FieldMedicine
TopicOcular and Laser Science Research
Canadian institutionsLaurentian University
Fundersnot available
KeywordsConsciousnessTest (biology)PsychologyComputer scienceGeology
DOInot available

Abstract

fetched live from OpenAlex

Whereas a multitude of solar and geomagnetic variables were not correlated with significant deviations in continuous measurements from random physical systems (Random Event Generators), these variables were moderately correlated with REG output during periods of intention. The scalar components (2 –10 nT) of the Interplanetary Magnetic Field (r = ~0.50) and global geomagnetic activity (r = ~0.55) were significantly correlated with REG deviations during the second minute of intention. Significance compared with nonsuccessful deviations occurred during periods of intention when the Solar Radio Flux was about 20 units (2∙10−21 W∙m −2 Hz −1) higher. The polarity of the deviation was different within a Faraday (echoic) chamber than in a normal environment as well as between genders. The amount of energy associated with the increase in geomagnetic activity within the volume of human cerebrum is remarkably similar to the gravitational energy within this mass because of minute variations in G (the Gravitational Constant). These results indicate that a subset of variance shared across several components of the ambient heliogeophysical environment may be a significant mediator of intention-coupled changes in random variations in p-n junction devices, and those discrete energies associated with intrinsic variations in G may be relevant.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.118
GPT teacher head0.496
Teacher spread0.378 · 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 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

Citations2
Published2014
Admission routes1
Has abstractyes

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