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Record W4396509975 · doi:10.3847/2515-5172/ad43d8

Minimum Mars Climate Sounder Retrieval Altitudes Reveal Cloud Altitudes at Aphelion and Stranded High-altitude Dust Following the MY34 Global Dust Storm on Mars

2024· article· en· W4396509975 on OpenAlexfundno aff
Alex C. Innanen, John E. Moores, M. E. Landis, Victoria Concepcion

Bibliographic record

VenueResearch Notes of the AAS · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMars Exploration ProgramDust stormStormAltitude (triangle)Environmental scienceAstrobiologyAtmospheric sciencesAtmosphere of MarsEffects of high altitude on humansMeteorologyMartianGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract The Mars Climate Sounder (MCS) has been observing Mars’ atmosphere since the Mars Reconnaissance Orbiter’s arrival in 2006. While MCS can theoretically observe down to the surface, in practice the presence of aerosols such as dust and water ice can cut off retrievals at altitudes tens of kilometers above the surface. We examine the minimum cut-off altitudes of MCS temperature retrievals over the course of MCS's mission for the area around Gale Crater. We see a preference for cut-off altitudes above 20 km in the cloudy season and greater variation in preference in the dusty season. These cut-off altitudes can be used to infer cloud altitudes and to track the decay of significant dust storms as well as seeing the effects of stranded dust in the MY 34 global dust storm.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.341
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 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueResearch Notes of the AASSame topicPlanetary Science and ExplorationFrench-language works237,207