MétaCan
Menu
Back to cohort
Record W2769922342 · doi:10.1002/2017je005456

Impact Crater Morphology and the Structure of Europa's Ice Shell

2017· article· en· W2769922342 on OpenAlexfundno aff

Bibliographic record

VenueJournal of Geophysical Research Planets · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImpact craterShell (structure)ConvectionElectrical conductorThermalTemperature gradient

Abstract

fetched live from OpenAlex

Abstract We performed numerical simulations of impact crater formation on Europa to infer the thickness and structure of its ice shell. The simulations were performed using iSALE to test both the conductive ice shell over ocean and the conductive lid over warm convective ice scenarios for a variety of conditions. The modeled crater depth‐diameter is strongly dependent on the thermal gradient and temperature of the warm convective ice. Our results indicate that both a fully conductive (thin) shell and a conductive‐convective (thick) shell can reproduce the observed crater depth‐diameter and morphologies. For the conductive ice shell over ocean, the best fit is an approximately 8 km thick conductive ice shell. Depending on the temperature (255–265 K) and therefore strength of warm convective ice, the thickness of the conductive ice lid is estimated at 5–7 km. If central features within the crater, such as pits and domes, form during crater collapse, our simulations are in better agreement with the fully conductive shell (thin shell). If central features form well after the impact, however, our simulations suggest that a conductive‐convective shell (thick shell) is more likely. Although our study does not provide a firm conclusion regarding the thickness of Europa's ice shell, our work indicates that Valhalla class multiring basins on Europa may provide robust constraints on the thickness of Europa's ice shell.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.394

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.343
Teacher spread0.317 · 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

Citations46
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Geophysical Research PlanetsSame topicAstro and Planetary ScienceFrench-language works237,207