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Record W2088501173 · doi:10.1088/2041-8205/712/1/l40

METHANE GAS STABILIZES SUPERCOOLED ETHANE DROPLETS IN TITAN'S CLOUDS

2010· article· en· W2088501173 on OpenAlexafffund
Chia C. Wang, E. Kathrin Lang, Ruth Signorell

Bibliographic record

VenueThe Astrophysical Journal Letters · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaAlfred P. Sloan Foundation
KeywordsTitan (rocket family)SupercoolingAstrobiologyMethaneAtmosphere (unit)Atmosphere of TitanAtmospheric sciencesInfraredChemical physicsPhysicsMeteorologyChemistryAstronomyOrganic chemistry

Abstract

fetched live from OpenAlex

Strong evidence for ethane clouds in various regions of Titan's atmosphere has recently been found. Ethane is usually assumed to exist as ice particles in these clouds, although the possible role of liquid and supercooled liquid ethane droplets has been recognized. Here, we report on infrared spectroscopic measurements of ethane aerosols performed in the laboratory under conditions mimicking Titan's lower atmosphere. The results clearly show that liquid ethane droplets are significantly stabilized by methane gas which is ubiquitous in Titan's nitrogen atmosphere—a phenomenon that does not have a counterpart for water droplets in Earth's atmosphere. Our data imply that supercooled ethane droplets are much more abundant in Titan's clouds than previously anticipated. Possibly, these liquid droplets are even more important for cloud processes and the formation of lakes than ethane ice particles.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.217
Teacher spread0.211 · 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 designSimulation or modeling
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

Citations13
Published2010
Admission routes2
Has abstractyes

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