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Record W2171689965 · doi:10.3189/2013aog62a146

Large-scale ice thickness distribution of first-year sea ice in spring and summer north of Svalbard

2012· article· en· W2171689965 on OpenAlexaff
Angelika H. H. Renner, Stefan Hendricks, Sebastian Gerland, Justin Beckers, Christian Haas, Thomas Krumpen

Bibliographic record

VenueAnnals of Glaciology · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Alberta
FundersFramsenteret
KeywordsSea iceFreeboardArctic ice packGeologyAntarctic sea iceSea ice thicknessFast iceSpring (device)CryosphereDrift iceSnowClimatologyIce divideArcticPancake iceIce streamOceanographyGeomorphology

Abstract

fetched live from OpenAlex

Abstract The large-scale thickness distribution of sea ice was measured during several campaigns in the European Arctic north of Svalbard from 2007 using an airborne electromagnetic induction device. In August 2010 and April-May 2011, this was complemented by extensive on-ice work including measurements of snow thickness and freeboard. Ice thicknesses show a clear difference between the seasons, with thicker ice during spring than in summer. In spring 2011, negative freeboard and flooding were observed as a result of the extensive snow cover. We find that the characteristics of the first-year sea ice allow combining observations from different years. The ice thickness in the marginal ice zone increases with increasing latitude and increasing distance to the ice edge; however, in the inner ice pack from ∼100 km from the ice edge the thickness remains almost constant. Modal ice thickness in spring reaches 2.4 m whereas in summer it is 1.0–1.4 m. Our study provides new insight into ice thickness distributions of a typical ice cover consisting of mainly first- and second-year ice, which may become the dominant ice type in the Arctic in the future.

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 score0.003
Threshold uncertainty score0.330

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 head0.027
GPT teacher head0.258
Teacher spread0.231 · 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

Citations29
Published2012
Admission routes1
Has abstractyes

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