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Record W7132628334

Features of Earth's local magnetic field in different locations

2019· article· en· W7132628334 on OpenAlexaboutno aff
Alfonsas Vainoras, Narimantas Listopadskis, Mantas Landauskas, Zenonas Navickas, Rollin McCraty

Bibliographic record

VenueLithuanian University of Health Sciences · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetometerMagnetic fieldField (mathematics)Interval (graph theory)Space weatherInterplanetary magnetic fieldSpectral densityIntensity (physics)
DOInot available

Abstract

fetched live from OpenAlex

At this time that are 6 ultra-sensitive magnetometers, which constantly continuously record the Earth's time carying magnetic field which are located in California USA, Canada, Lithuania, Saudi Arabia, South Africa and New Zealand. Numerous scientific publications have show significant interrelations between changes in the Earth's magnetic field and human health. We studied the data recorded from the magnetometers located in Canada, Lithuania, South Africa and New Zeland during the 2016-2017 years. The spectrums that relect the Shumann resonancies were divided into frequency intervals according to the EEG clasification corresponding with the main Shumann resonancess spectrum bands [0-3.5 Hz], [3.5-7 Hz], [7-15 Hz], [15-32 Hz], and [32-66 Hz]. The power of of Schuman resonances spectrums in every frequency interval was calculated as a average (A) of the power and as a median (M) of it. The difference between (A-M) show intensity of nontypical fluctuations in Earth magnetic field. Those nontypical fluctuations, we think, is more related to the space weather influences and solar wind while instant Earth magnetic, field fluctuations are more related to more slow median changes. [...].

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)0.000
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 score0.242
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.012
GPT teacher head0.262
Teacher spread0.250 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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