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Record W2902965781 · doi:10.1029/2018je005781

Compaction and Melt Transport in Ammonia‐Rich Ice Shells: Implications for the Evolution of Triton

2018· article· en· W2902965781 on OpenAlexaff

Bibliographic record

VenueJournal of Geophysical Research Planets · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsThe Scarborough Hospital
FundersNational Aeronautics and Space Administration
KeywordsShell (structure)Eutectic systemThermalCompactionPancake iceAmorphous iceThickeningSea ice growth processesClear ice

Abstract

fetched live from OpenAlex

Abstract Ammonia, if present in the ice shells of icy satellites, could lower the temperature for the onset of melting to 176 K and create a large temperature range where partial melt is thermally stable. The evolution of regions of ammonia‐rich partial melt could strongly influence the geological and thermal evolution of icy bodies. For melt to be extracted from partially molten regions, the surrounding solid matrix must deform and compact. Whether ammonia‐rich melts sink to the subsurface ocean or become frozen into the ice shell depends on the compaction rate and thermal evolution. Here we construct a model for the compaction and thermal evolution of a partially molten, ammonia‐rich ice shell in a one‐dimensional geometry. We model the thickening of an initially thin ice shell above an ocean with 10% ammonia. We find that ammonia‐rich melts can freeze into the upper 5 to 10 km of the ice shell, when ice shell thickening is rapid compared to the compaction rate. The trapping of near‐surface volatiles suggests that, upon reheating of the ice shell, eutectic melting events are possible. However, as the ice shell thickening rate decreases, ammonia‐rich melt is efficiently excluded from the ice shell and the bulk of the ice shell is pure water ice. We apply our results to the thermal evolution of Neptune's moon Triton. As Triton's ice shell thickens, the gradual increase of ammonia concentration in Triton's subsurface ocean helps to prevent freezing and increases the predicted final ocean thickness by up to 50 km.

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 score0.201
Threshold uncertainty score0.254

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 communication0.0000.000
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 head0.046
GPT teacher head0.354
Teacher spread0.308 · 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

Citations32
Published2018
Admission routes1
Has abstractyes

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