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Record W4320016305 · doi:10.1016/j.asr.2023.01.017

Ionospheric electron density over Resolute Bay according to E-CHAIM model and RISR radar measurements

2023· article· en· W4320016305 on OpenAlexafffundabout
Bion Larson, A. V. Koustov, David R. Themens, R. G. Gillies

Bibliographic record

VenueAdvances in Space Research · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of CalgaryUniversity of New BrunswickUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyUniversity of CalgaryNational Science Foundation
KeywordsIonosphereDaytimeElectron densityIncoherent scatterBayAtmospheric sciencesF regionRadarEnvironmental sciencePhysicsElectronMeteorologyGeologyGeophysicsOceanography

Abstract

fetched live from OpenAlex

In this study, predictions of the E-CHAIM ionospheric model are compared with measurements by the incoherent scatter radars RISR at Resolute Bay, Canada, in the northern polar cap. Reasonable coverage was available for all seasons except winter for which no conclusions were drawn. It is shown that ratios of the model-to measured electron densities are close to unity in the central part of the F layer, around its peak. This is particularly evident for summer daytime. Distributions of the ratios are wider for other seasons indicating larger number of cases when the model underestimates or overestimates. E-CHAIM underestimates the electron density at ionospheric topside and bottomside by ∼ 10–20 %. At the bottomside, the underestimations are strongest in summer and equinoctial nighttime. At the topside, the underestimations are strongest in autumn nighttime. Model overestimations are noticeable in the middle part of the F layer during dawn hours in autumn. Overall, the model tends to not predict highest-observed peak electron densities and the largest-observed heights of the peak.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.356
Teacher spread0.321 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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