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Record W2112084594 · doi:10.5194/tc-8-1205-2014

The growth of sublimation crystals and surface hoar on the Antarctic plateau

2014· article· en· W2112084594 on OpenAlexaff
Jean‐Charles Gallet, Florent Dominé, Joël Savarino, Marie Dumont, E. Brun

Bibliographic record

Venue˜The œcryosphere · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsTakuvik Joint International LaboratoryUniversité LavalMakivik Corporation
FundersNorges ForskningsrådInstitut Polaire Français Paul Emile Victor
KeywordsSublimation (psychology)SnowAtmospheric sciencesIce crystalsSurface energyEnvironmental sciencePrecipitationGeologyMaterials scienceMeteorologyGeographyGeomorphology

Abstract

fetched live from OpenAlex

Abstract. On the Antarctic plateau, precipitation quantities are so low that the surface mass budget is for an important part determined by exchanges of water vapor between the snow surface and the atmosphere surface. At Dome C (75° S, 123° E), we have frequently observed the growth of crystals on the snow surface under calm sunny weather. Here we present the time variations of specific surface area (SSA) and density of these crystals. Using the detailed snow model Crocus, we conclude that the formation of these crystals was very likely due to the nighttime formation of surface hoar crystals and to the daytime formation of sublimation crystals. These latter crystals form by processes similar to those involved in the formation of frost flowers on young sea ice. The formation of these crystals impacts the albedo, mass and energy budget of the Antarctic plateau. In particular, the SSA variations of the surface layer can induce an instantaneous forcing at the snow surface up to −10 W m−2 at noon, resulting in a surface temperature drop of 0.45 K. This result confirms that snow SSA is a crucial variable to consider in the energy budget and climate of snow-covered surfaces.

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 score0.032
Threshold uncertainty score0.789

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 studies0.0010.000
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 head0.014
GPT teacher head0.194
Teacher spread0.180 · 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

Citations47
Published2014
Admission routes1
Has abstractyes

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