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Record W2064215233 · doi:10.1186/bf03352033

Modelling the disturbance caused by a dc-electrified railway to geomagnetic measurements

2007· article· en· W2064215233 on OpenAlexaffabout
Risto Pirjola, Larry Newitt, D. H. Boteler, L. Trichtchenko, P. Fernberg, L McKee, D. W. Danskin, G Jansen van Beek

Bibliographic record

VenueEarth Planets and Space · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsEarth's magnetic fieldTrainComputationMagnetic fieldObservatoryMATLABNoise (video)FortranPhysicsComputer science

Abstract

fetched live from OpenAlex

Abstract Magnetic fields created by a dc-electrified railway are a nuisance to the operation of a geomagnetic observatory and also disturb other electromagnetic studies. Theoretical formulas that enable quantitative estimates of the magnetic effect of a dc railway including leakage currents in the ground are presented in this paper. They are illustrated by numerical examples. The validity of the theoretical model was verified by measurements carried out in the vicinity of a nearly north-south railway in Calgary, Canada. The earth structure in that area is approximately layered, which is an assumption included in the theoretical model. The agreement between the measured magnetic fields due to trains and the theoretical values is good. Numerical computations indicate that magnetic fields larger than the maximum allowable noise level (assumed to be about 10 pT) at today’s magnetic observatories may extend to distances of tens of kilometres from a railway. We have prepared computer programs based on the theoretical formulas in the MatLab, Octave, FORTRAN and IDL languages, in which the locations (i.e. the latitudes and the longitudes) of the point of observation, of the feeding substations and of the trains, together with the feeding and leakage currents and the heights of the feeding lines, can be given as inputs.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.818
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.226
Teacher spread0.200 · 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 designOther design
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

Citations13
Published2007
Admission routes2
Has abstractyes

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