MétaCan
Menu
Back to cohort
Record W2981032055 · doi:10.1029/2019ja027258

HIWIND Observation of Summer Season Polar Cap Thermospheric Winds

2019· article· en· W2981032055 on OpenAlexafffundabout
Qian Wu, D. J. Knipp, Jing Liu, Wenbin Wang, R. H. Varney, R. G. Gillies, P. J. Erickson, Michael Greffen, A. S. Reimer, I. Häggström, Geonhwa Jee, Young‐Sil Kwak

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersNational Science Foundation of Sri LankaCanada Foundation for InnovationNational Aeronautics and Space Administration
KeywordsThermosphereAtmospheric sciencesIonosphereNoonPhysicsDaytimeZonal and meridionalF regionFlux (metallurgy)Electron precipitationPolarEnvironmental scienceElectron densityElectronGeophysicsPlasmaAstronomyMagnetosphere

Abstract

fetched live from OpenAlex

Abstract HIWIND (High altitude Interferometer WIND experiment) is a balloon‐borne Fabry Perot interferometer for daytime thermospheric wind observations. In this paper, we examine the summer polar cap thermospheric winds observed by HIWIND with the RISR‐C (Resolute Incoherent Scatter Radar‐Canada) observed ion drifts and electron densities. We also perform National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model simulations to compare with the HIWIND and RISR‐C observations. The standard Thermosphere Ionosphere Electrodynamics General Circulation Model underestimates the high‐latitude electron density and overestimates the thermospheric winds. The discrepancies between modeled and observed meridional winds are large near midnight and noon. After increasing the energy flux in the polar cap drizzle, the simulated electron density is comparable with the RISR‐C observations. However, large discrepancies with the HIWIND‐observed thermospheric winds persist. The cause of the model versus observation discrepancy in winds is probably due to the processes outside the polar cap.

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)0.001
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 score0.176
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.306
Teacher spread0.279 · 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

Citations13
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Geophysical Research Space PhysicsSame topicIonosphere and magnetosphere dynamicsFrench-language works237,207