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Record W2519706117 · doi:10.1002/2016jd025211

Mars' atmosphere: The sister planet, our statistical twin

2016· article· en· W2519706117 on OpenAlexaff
Wilbur Chen, S. Lovejoy, Jan‐Peter Müller

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsMcGill University
FundersScience and Technology Facilities Council
KeywordsMars Exploration ProgramPlanetAtmosphere (unit)TurbulenceAstrobiologyAtmosphere of MarsMartianPhysicsGeologyAtmospheric sciencesMeteorologyAstronomy

Abstract

fetched live from OpenAlex

Abstract Satellite‐based Martian reanalyses have allowed unprecedented comparisons between our atmosphere and that of our sister planet, underlining various similarities and differences in their respective dynamics. Yet by focusing on large scale structures and deterministic mechanisms they have improved our understanding of the dynamics only over fairly narrow ranges of (near) planetary scales. However, the Reynolds numbers of the flows on both planets are larger than 10 11 and dissipation only occurs at centimetric (Mars) or millimetric scales (Earth) so that over most of their scale ranges, the dynamics are fully turbulent. In this paper, we therefore examine the high‐level, statistical, turbulent laws for the temperature, horizontal wind, and surface pressure, finding that Earth and Mars have virtually identical statistical exponents so that their statistics are very similar over wide ranges. Therefore, it would seem that with the exception of certain aspects of the largest scales (such as the role of dust in atmospheric heating on Mars, or of water in its various phases on Earth), that the nonlinear dynamics are very similar. We argue that this is a prediction of the classical laws of turbulence when extended to planetary scales and that it supports our use of turbulent laws on both planetary atmospheres.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.036
GPT teacher head0.320
Teacher spread0.284 · 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.

Study designNot applicable
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

Citations20
Published2016
Admission routes1
Has abstractyes

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