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Explanation for the increase in high altitude water on Mars observed by NOMAD during the 2018 global dust storm

2020· article· en· W3041291370 on OpenAlexaff
Lori Neary, Frank Daerden, Shohei Aoki, J. A. Whiteway, R. T. Clancy, M. D. Smith, S. Viscardy, Justin Erwin, Ian Thomas, Ann Carine Vandaele

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
Fundersnot available
KeywordsMars Exploration ProgramAtmospheric sciencesAltitude (triangle)Dust stormWater vaporEnvironmental scienceAtmosphere of MarsAtmosphere (unit)StormEffects of high altitude on humansMartianMeteorologyAstrobiologyPhysics

Abstract

fetched live from OpenAlex

Using the GEM-Mars three-dimensional general circulation model (GCM), we examine the mechanism responsible for the enhancement of water vapour in the upper atmosphere as measured by the Nadir and Occultation for MArs Discovery (NOMAD) instrument onboard ExoMars Trace Gas Orbiter (TGO) during the 2018 global dust storm on Mars. Experiments with different prescribed vertical profiles of dust show that when more dust is present higher in the atmosphere, the temperature increases and the amount of water ascending over the tropics is not limited by saturation until reaching heights of 70-100 km. The warmer temperatures allow more water to ascend to the mesosphere. The simulation of enhanced high-altitude water abundances is very sensitive to the vertical distribution of the dust prescribed in the model. The GEM-Mars model includes gas-phase photochemistry, and these simulations show how the increased water vapour over the 40-100 km altitude range results in the production of high-altitude atomic hydrogen which can be linked to atmospheric escape.

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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.213
Teacher spread0.188 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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