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Record W4220795963 · doi:10.5194/egusphere-egu22-5792

Utilisation of geomagnetic data and indices for GIC applications

2022· preprint· en· W4220795963 on OpenAlex
L. Trichtchenko

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
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsEarth's magnetic fieldGeomagnetically induced currentSpace weatherMeteorologyEnvironmental scienceGeomagnetic stormPhysicsMagnetic field

Abstract

fetched live from OpenAlex

<p>The development of mitigation capabilities to counteract the detrimental impacts of space weather on critical ground infrastructure, such as power lines, pipelines and cables, depends on the availability of the observations of their causes as well as monitoring of the subsequent results.<br>Although direct monitoring of critical infrastructure response to GeoMagnetic Disturbances (GMD) has become more advanced in recent years, observations of geomagnetic variations continue to play the most important role in all aspects of development of safe and robust operational procedures and technology, from the forecast of geomagnetically induced currents (GIC) to their climatological studies.<br>This presentation shows how different types of geomagnetic data are utilised, from 3-hour and 1-hour geomagnetic indices to 1 sec. geomagnetic data, and from real-time to multi-year climatology in order to provide forecasts of GIC, identify the effects of different geomagnetic patterns on infrastructure response or provide “climatology” for network design considerations.  </p>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.993

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)0.0080.000

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 head0.049
GPT teacher head0.274
Teacher spread0.225 · 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
Published2022
Admission routes1
Has abstractyes

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