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Record W4225778002 · doi:10.1029/2022ja030355

The Use of Invalid Polar Cap South (PCS) Indices in Publications

2022· article· en· W4225778002 on OpenAlexfundno aff
Peter Stauning

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersDanmarks Tekniske UniversitetAlberta Agricultural Research Institute
KeywordsAeronomySpace weatherSolar windMagnetosphereMeteorologySpace physicsIndex (typography)Earth's magnetic fieldSpace researchLibrary scienceGeographyComputer scienceGeologyPhysicsGeophysicsAstronomyAtmosphere (unit)World Wide Web

Abstract

fetched live from OpenAlex

Abstract Polar Cap (PC) indices, PCN North based on magnetic observations from Qaanaaq (THL) in Greenland and PCS South based on magnetic data from Vostok in Antarctica, are very useful indices for studies of solar wind‐magnetosphere interactions and for space weather monitoring and forecasts. PCN indices are issued from the Danish Space Research Institute (DTU Space) while PCS indices are issued from the Arctic and Antarctic Research Institute (AARI) in St Petersburg. Unfortunately, series of invalid PCS values have been provided from AARI and used in a number of publications issued since the PC index concept was endorsed by the International Association of Geomagnetism and Aeronomy (IAGA) in 2013. In spite of the disclosure of the failure in the PCS indices in 2018, publishing at the IAGA‐supported International Service of Geomagnetic Indices of the invalid indices has continued up to now (2022). The present contribution defines the invalidating features of the PCS indices in question, conveys examples, and discusses the applications in peer‐reviewed publications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.119
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.119
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0180.033
Science and technology studies0.0020.003
Scholarly communication0.0160.007
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.005

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 head0.068
GPT teacher head0.327
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
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

Citations10
Published2022
Admission routes1
Has abstractyes

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