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Record W2101084788 · doi:10.5194/angeo-27-2399-2009

Joint observations of a traveling ionospheric disturbance with the Paratunka OMTI camera and the Hokkaido HF radar

2009· article· en· W2101084788 on OpenAlexafffund
A. V. Koustov, Н. Нишитани, P. V. Ponomarenko, K. Shiokawa, Shin Suzuki, Б. М. Шевцов, J. W. MacDougall

Bibliographic record

VenueAnnales Geophysicae · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsWestern UniversityUniversity of Saskatchewan
FundersJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaMinistry of Education, Culture, Sports, Science and Technology
KeywordsIonosphereGeologyAirglowRadarPerturbation (astronomy)GeophysicsSkyRay tracing (physics)GeodesyIncoherent scatterPhysicsOpticsAstronomyAtmospheric sciences

Abstract

fetched live from OpenAlex

Abstract. On 10 September 2007 between 10:00 and 14:00 UT, the OMTI all-sky imager at Paratunka (Kamchatka, Russia, GLAT~52°) observed the onset and south-western progression of a localized depletion region in the airglow intensity. The perturbation, while being stretched in the NW-SE direction, crossed the entire field of view of the camera. During the event, the Hokkaido SuperDARN HF radar was monitoring echoes in the Paratunka longitudinal sector. It was detecting a localized band of ground scatter echoes progressing equatorward synchronously with the motion of the optical perturbation. It is suggested that both features resulted from the onset and south-western progression of a localized region with enhanced electric field that influenced the distribution of the plasma density in the ionosphere. Modeling of the HF ground scatter dynamics based on numerical ray tracing demonstrated qualitative consistency with the observations.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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

Citations8
Published2009
Admission routes2
Has abstractyes

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