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Record W2071694890 · doi:10.1002/2013gl058748

Global imaging of polar cap patches with dual airglow imagers

2013· article· en· W2071694890 on OpenAlexaboutno aff
Keisuke Hosokawa, S. Taguchi, K. Shiokawa, Yuichi Otsuka, Yasunobu Ogawa, M. J. Nicolls

Bibliographic record

VenueGeophysical Research Letters · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAirglowNoonPolarDuskLatitudeLocal timeBaySkyDaytimeMidnightGeologyPhysicsAtmospheric sciencesGeodesyAstrophysicsAstronomyOceanography

Abstract

fetched live from OpenAlex

During a 2 h interval from 2240 to 2440 UT on 12 November 2012, regions of increased 630.0 nm airglow emissions were simultaneously detected by dual all‐sky imagers in the polar cap, one at Longyearbyen, Norway (78.1°N, 15.5°E) and the other at Resolute Bay, Canada (74.7°N, 265.1°E). The Resolute Bay incoherent scatter radar observed clear enhancements of the F region electron density up to 10 12 m −3 within these airglow structures which indicates that these are optical manifestations of polar cap patches propagating across the polar cap. During this interval of simultaneous airglow imaging, the nightside/dawnside (dayside/duskside) half of the patches was captured by the imager at Longyearbyen (Resolute Bay). This unique situation enabled us to estimate the dawn‐dusk extent of the patches to be around 1500 km, which was at least 60–70% of the width of the antisunward plasma stream seen in the Super Dual Auroral Radar Network convection maps. In contrast to the large extent in the dawn‐dusk direction, the noon‐midnight thickness of each patch was less than 500 km. These observations demonstrate that there exists a class of patches showing cigar‐shaped structures. Such patches could be produced in a wide range of local time on the dayside nearly simultaneously and spread across many hours of local time soon after their generation.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.982

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)0.0010.000

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 head0.258
Teacher spread0.250 · 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 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

Citations165
Published2013
Admission routes1
Has abstractyes

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