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Record W3172483355 · doi:10.5194/egusphere-egu21-9430

Combining satellite and lidar measurements to investigate the sodium nightglow

2021· article· en· W3172483355 on OpenAlexaff
Julia Koch, Adam Bourassa, Chris Roth, N. D. Lloyd, C. Y. She

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAirglowLidarSodiumSatelliteGround stateAtomic physicsEnvironmental sciencePhysicsChemistryMeteorologyAtmospheric sciencesOpticsAstronomy

Abstract

fetched live from OpenAlex

Using a combination of different measurement techniques is important to understand the numerous processes happening in the MLT-region. One of those processes is the excitation of atomic sodium by reaction with ozone which leads to emission of electromagnetic radiation: a phenomenon called Airglow. Although the sodium excitation mechanism was already proposed in 1939 by Sidney Chapman and further investigation was done by a great number of scientists, there are still some key parameters that are not well-known today. One of those parameters is the branching ratio fA which determines the amount of sodium in the excited state. Exact knowledge of this value would offer the opportunity to use Na-nightglow measurements to determine sodium profiles in the MLT-region. In this study we used both, satellite measurements and ground-based Lidar measurements to help approach a more reliable branching ratio fA. By comparing measurements that were made by the two instruments OSIRIS on Odin (Satellite) and the Lidar of the Colorado State University (ground-based) we found a branching ratio fA of 0.064 +- 0.028.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.227
Teacher spread0.209 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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