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Record W4231632819 · doi:10.32920/ryerson.14656296

Characterization of noise in the lightning current derivative signals measured at the CN tower

2021· preprint· en· W4231632819 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRadio Wave Propagation Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTowerLightning (connector)Noise (video)Interference (communication)SIGNAL (programming language)Electrical engineeringCurrent (fluid)Lightning strikeAcousticsDerivative (finance)Electromagnetic interferenceMeteorologyComputer sciencePhysicsEngineeringTelecommunicationsLightning arresterStructural engineering

Abstract

fetched live from OpenAlex

Simultaneous measurements of parameters of CN Tower lightning strikes have been performed since 1991. The current derivative signals measured, are corrupted by a 100 kHz oscillating interference. This noise has caused substantial limitations on the usage of the CN Tower lightning current data. As a result, we became motivated to characterize it and search for its source. Furthermore identifying the low-frequency noise is expected to help in its removal and avoid it altogether in future installations. This thesis proves that the low-frequency noise corrupting the lightning current derivative signals is the Loran-C radionavigation signal. This finding is a major contribution not only for the CN Tower lightning project but also for any other research related to measurement of lightning at tall structures. Researchers and experimentalists should be aware of the existence of the Loran-C signal and take the necessary precautions to avoid its interference effect.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.566

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 head0.038
GPT teacher head0.256
Teacher spread0.218 · 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

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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