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Record W2793417392 · doi:10.5194/angeo-36-891-2018

Differentiating diffuse auroras based on phenomenology

2018· article· en· W2793417392 on OpenAlexafffund
Eric Grono, E. Donovan

Bibliographic record

VenueAnnales Geophysicae · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaDanmarks Tekniske UniversitetNational Aeronautics and Space Administration
KeywordsConvectionPhysicsPhenomenology (philosophy)GeophysicsAstrophysicsGeologyMechanics

Abstract

fetched live from OpenAlex

There is mounting evidence which suggests that pulsating auroral patches often move with convection. This study is an initial step at identifying the differences between patches that move with convection and those that do not. While many properties of pulsating patches vary, here we outline criteria for separating pulsating auroral patches into three categories based on two properties: their structural stability and the spatial extent of their pulsations. Patchy aurora is characterized by stable structures whose pulsations are limited to small regions. Patchy pulsating aurora consists of stable patches whose pulsations are far less subtle and occur throughout much of their area. Amorphous pulsating auroral structures are unstable – very rapidly evolving – and can pulsate over their entire area. The speed with which amorphous pulsating aurora evolves makes their motion difficult to ascertain and seems unrelated to the E × B drifting of cold, equatorial plasma.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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.

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

Citations46
Published2018
Admission routes2
Has abstractyes

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