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Record W2619938457 · doi:10.14430/arctic4644

Slush-Ice Berm Formation on the West Coast of Alaska

2017· article· en· W2619938457 on OpenAlexaffvenue
Laura Eerkes-Medrano, David Atkinson, Hajo Eicken, Bill Nayokpuk, Harvey Sookiayak, Eddie Ungott, Winton Weyapuk

Bibliographic record

VenueARCTIC · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Victoria
FundersNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsBermSlushGeologyShoreSwashCobbleWinter stormSnowPremeltingGeomorphologyOceanographyHydrology (agriculture)Geotechnical engineering

Abstract

fetched live from OpenAlex

Some coastal communities in western Alaska have observed the occurrence of “slush-ice berms.” These features typically form during freeze-up, when ice crystal – laden water accumulates in piles on the shore. Slush-ice berms can protect towns from storm surge, and they can limit access to the water. Local observations from the communities of Gambell, Shaktoolik, Shishmaref, and Wales were synthesized to develop a taxonomy of slush-ice berm types and a conceptual process model that describes how they form and decay. Results indicated two types of slush-ice berm formation processes: in situ (forming in place) and advective (pushed in by storm winds). Several formation mechanisms were noted for the crystals that compose in situ berms. Cold air temperatures cool the surface of the water, and winds that translate surface cooling through a greater depth aid crystal formation. Snow landing in the water cools via melting of the snow and by contributing crystals directly to the water. A negative surge can expose the wet beach to cold air, allowing crystals to form on the beach, which are then picked up by waves. Slush crystals for advective berm events form offshore. Winds move the slush towards shore, where it accumulates, and wind-induced waves move it up onto the beach.

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 score0.033
Threshold uncertainty score0.916

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.215
Teacher spread0.198 · 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

Citations13
Published2017
Admission routes2
Has abstractyes

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