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Record W2746963715 · doi:10.1002/2016ja023105

A theoretical framework for the changing spectral properties of meter‐scale Farley‐Buneman waves between 90 and 125 km altitudes

2016· article· en· W2746963715 on OpenAlexaff
J.‐P. St.‐Maurice, Jorge L. Chau

Bibliographic record

VenueJournal of Geophysical Research Space Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDoppler effectInstabilityAltitude (triangle)IonSpectral linePhysicsMetreComputational physicsElectronDoppler broadeningAsymmetryAtomic physicsRange (aeronautics)MechanicsNuclear physicsMaterials scienceMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Stimulated by recent observations described in a companion paper, we have revisited existing theories of the Farley‐Buneman instability throughout the altitude range 90 to 125 km. We have assumed that the irregularities detected by radars at a given altitude are dominated by structures moving at the threshold speed in a direction associated with maximum linear growth rate conditions. We included recent nonisothermal electron and ion theories, which can modify threshold speeds by considerable amounts. We included altitude‐dependent models of ion and electron temperature and of the ion motion in the phase velocity calculations. Our treatment of the instability explains why some spectra are slow (Doppler shifts typically 200 m/s) and narrow, while others are fast (1500 m/s or close to the E × B ) and narrow. These narrow spectra have all the characteristics of what has been labeled as “Type III” and “Type IV” in the past. Our calculations also offer an explanation for the observation of a strong asymmetry in the number of events with positive Doppler shifts near the nominal ion‐acoustic speed and those with negative Doppler shifts of the same magnitude.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.315
Teacher spread0.285 · 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 designTheoretical or conceptual
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

Citations19
Published2016
Admission routes1
Has abstractyes

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