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IMPACT OF GEOMAGNETIC STORMS, LUNAR CYCLES AND DAYS OF THE WEEK ON CAR ACCIDENTS INJURING PEOPLE IN TERMS OF THEIR POTENTIAL IMPACT ON ROAD USERS IN VINNYTSIA AND THE REGION, UKRAINE

2022· article· en· W4226085689 on OpenAlexaboutno aff
A. Y. Kulyk, Віктор Ревенок, Aleksandr I. Nikolskyy, K. V. Dobrovolska

Bibliographic record

VenueInformation Technology and Computer Engineering · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsnot available
Fundersnot available
KeywordsThursdayEarth's magnetic fieldQuarter (Canadian coin)StormMeteorologyNew moonEnvironmental scienceGeographyArchaeologyPhysics

Abstract

fetched live from OpenAlex

The study analyzes the impact of geomagnetic storms, lunar cycles and days of the week on car accidents with injuries to people in terms of their potential impact on road users. The study was conducted on the basis of data from 2015 to 2020 in the city of Vinnytsia and region, Ukraine. The effect of geomagnetic storms was estimated according to the values of the Kr index and was weak at a level less than 0.05. The analysis by lunar cycles took place in four lunar phases. An increase in the number of accidents was observed only in the period of the new moon - the first quarter of the cycle. The number of accidents increased on Thursday and Friday relative to the days of the week, and the number of accidents decreased from Monday to Tuesday during the last two years of observation.

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 score0.569
Threshold uncertainty score0.191

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 head0.002
GPT teacher head0.181
Teacher spread0.179 · 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
Published2022
Admission routes1
Has abstractyes

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