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Record W1979662932 · doi:10.5194/angeo-28-711-2010

Observations of the auroral width spectrum at kilometre-scale size

2010· article· en· W1979662932 on OpenAlexafffundabout
Noora Partamies, M. Syrjäsuo, E. Donovan, Martin Connors, D. P. Charrois, D. J. Knudsen, Z. Kryzanowsky

Bibliographic record

VenueAnnales Geophysicae · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsAthabasca UniversityUniversity of Calgary
FundersCanadian Space AgencyChurchill Northern Studies CentreAcademy of Finland
KeywordsSkyScale (ratio)Instrumentation (computer programming)Remote sensingPhysicsField of viewArtifact (error)Range (aeronautics)Image resolutionOpticsAstrophysicsGeologyComputer scienceMaterials science

Abstract

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Abstract. This study examines auroral colour camera data from the Canadian Dense Array Imaging SYstem (DAISY). The Dense Array consists of three imagers with different narrow (compared to all-sky view) field-of-view optics. The main scientific motivation arises from an earlier study by Knudsen et al. (2001) who used All-Sky Imager (ASI) combined with even earlier TV camera observations (Maggs and Davis, 1968) to suggest that there is a gap in the distribution of auroral arc widths at around 1 km. With DAISY observations we are able to show that the gap is an instrument artifact and due to limited spatial resolution and coverage of commonly used instrumentation, namely ASIs and TV cameras. If the auroral scale size spectrum is indeed continuous, the mechanisms forming these structures should be able to produce all of the different scale sizes. So far, such a single process has not been proposed in the literature and very few models are designed to interact with each other even though the range of their favourable conditions do overlap. All scale-sizes should be considered in the future studies of auroral forms and electron acceleration regions, both in observational and theoretical approaches.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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.

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

Citations38
Published2010
Admission routes3
Has abstractyes

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