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Record W2124503147 · doi:10.5194/angeo-25-1531-2007

Daytime F2-layer negative storm effect: what is the difference between storm-induced and Q-disturbance events?

2007· article· en· W2124503147 on OpenAlexfundno aff
A. V. Mikhailov, A. H. Depueva, V. Depuev

Bibliographic record

VenueAnnales Geophysicae · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersNational Research Council CanadaNational Institute of Polar ResearchMax-Planck-GesellschaftRussian Foundation for Basic ResearchMassachusetts Institute of Technology
KeywordsDaytimeStormMillstone HillGeomagnetic stormAtmospheric sciencesDisturbance (geology)IonosphereSolsticeEnvironmental scienceF regionQUIETLatitudeMeteorologyPhysicsGeologyEarth's magnetic fieldGeophysicsGeodesy

Abstract

fetched live from OpenAlex

Abstract. Negative F2-layer storms related to geomagnetic activity and quiet-time disturbances (Q-disturbances) belong to different classes of events and exhibit different morphology. Mid-latitude daytime Q-disturbances, unlike the usual negative F2-layer storms, demonstrate NmF2 and hmF2 in-phase variations. An analysis of Millstone Hill ISR observations for usual and Q-disturbances has shown the difference in the controlling aeronomic parameter variations for the two classes of events. The decrease in atomic oxygen concentration provides the main contribution to the hmF2 decrease below the monthly median level during Q-disturbance events. Unlike the usual negative storms, the negative effect takes place in the whole topside ionosphere under Q-disturbance conditions. The difference is due to different effective plasma scale heights in the two cases. Clustering of the usual negative F2-layer disturbances around equinoxes and Q-disturbances around winter solstice, as well as different latitudinal variations for the occurrence of the two types of disturbances is due to their different formation mechanisms.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
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)0.0000.000

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 head0.010
GPT teacher head0.246
Teacher spread0.236 · 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

Citations32
Published2007
Admission routes1
Has abstractyes

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