MétaCan
Menu
Back to cohort
Record W2238987790 · doi:10.3847/0004-637x/821/1/17

COMPOSITIONAL SIMILARITIES AND DISTINCTIONS BETWEEN TITAN’S EVAPORITIC TERRAINS

2016· article· en· W2238987790 on OpenAlexaboutno aff

Bibliographic record

VenueThe Astrophysical Journal · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
Fundersnot available
KeywordsEvaporiteShoreComposition (language)TerrainFeature (linguistics)Absorption (acoustics)Polar

Abstract

fetched live from OpenAlex

ABSTRACT We document the similarities in composition between the equatorial basins Tui Regio, Hotei Regio, and other 5- μ m-bright materials, notably the north polar evaporites, by investigating the presence and extent of an absorption feature at 4.92 μ m. In most observations, Woytchugga Lacuna, Ontario Lacus, MacKay Lacus, deposits near Fensal, some of the lakes and dry lake beds south of Ligeia, and the southern shores of Kraken Mare share the absorption feature at 4.92 μ m observed in the spectra of Tui and Hotei. Besides Woytchugga and at Fensal, these 5- μ m-bright deposits are geomorphologically substantiated evaporites. Thus, the similarity in composition strengthens the hypothesis that Tui and Hotei once contained liquid. Other evaporite deposits, however, do not show the 4.92 μ m absorption, notably Muggel Lacus and the shores of Ligeia Mare at the north pole. This difference in composition suggests that there is more than one kind of soluble material in Titan’s lakes that can create evaporite and/or that the surface properties at the Visual and Infrared Mapping Spectrometer wavelength scale are not uniform between the different deposits (crystal size, abundance, etc.). Our results indicate that the surface structure, composition, and formation history of Titan’s evaporites may be at least as dynamic and complex as their Earth counterparts.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.423
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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.

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

Citations26
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe Astrophysical JournalSame topicAstro and Planetary ScienceFrench-language works237,207