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Record W6958788410 · doi:10.60841/000000005

Data from: Unveiling What Makes Saturn Ring: Quantifying the Amplitudes of Saturn's Planetary Normal-Mode Oscillations and Trends in C-Ring Properties Using Kronoseismology (VII)

2024· dataset· en· W6958788410 on OpenAlexaff

Bibliographic record

VenueUniversity of Idaho | Library · 2024
Typedataset
Languageen
FieldComputer Science
TopicTopic Modeling
Canadian institutionsCanadian Institute for Theoretical Astrophysics
FundersNational Aeronautics and Space Administration
KeywordsAmplitudeSaturnOccultationSatellitePlanetGravitational waveRotation (mathematics)Atmospheric wave

Abstract

fetched live from OpenAlex

Certain spiral density waves in Saturn's rings are generated through resonances with planetary normal modes, making them valuable probes of Saturn's internal structure. Previous research has primarily focused on the rotation rates of these waves. However, other characteristics of these waves also contain valuable information about the planet's interior. In this work, we investigate the amplitudes of the waves across the C-ring by analyzing high signal-to-noise profiles derived from phase-corrected averages of occultation profiles obtained by Cassini's Visual and Infrared Mapping Spectrometer (VIMS). By fitting these wave profiles to linear density wave models, we estimate the ring's surface mass density, mass extinction coefficient and effective kinematic viscosity at 34 locations in the C-ring, as well as the amplitude of the gravitational potential perturbations associated with 6 satellite resonances and 28 planetary normal mode resonances.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0110.018

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 head0.101
GPT teacher head0.265
Teacher spread0.164 · 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 designNot applicable
Domainnot available
GenreDataset

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

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