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Record W4214771211 · doi:10.1139/as-2021-0011

Properties and stratigraphy of polar ice patches in the Canadian High Arctic reveal their current resilience to warm summers

2022· article· en· W4214771211 on OpenAlexafffundvenueabout
Gautier Davesne, Daniel Fortier, Florent Dominé

Bibliographic record

VenueArctic Science · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalEffigis (Canada)Center for Northern Studies
FundersFonds de recherche du Québec – Nature et technologiesInstitut Polaire Français Paul Emile VictorNatural Sciences and Engineering Research Council of CanadaUniversity of WarwickArcticNetParks Canada
KeywordsGeologySea iceIce divideIce coreArctic ice packSnowMelt pondPhysical geographyAntarctic sea iceOceanographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Ice patches are ubiquitous in polar regions and are a key element for landscape evolution. We present new insights into polar desert ice patch formation based on snow and ice properties at Ward Hunt Island (Canadian High Arctic, 83°N). Our results demonstrate that ice patches are composed of two distinct units. The upper unit is characterized by very fine granular and bubbly ice with a clear oblique layering. By contrast, the lower unit is strikingly different with coarse crystals, lower porosity, and a high frequency of fractures. For both units, superimposed ice formation at the base of the deep snowpack stands out as the primary ice aggradation process. The distinct properties of the lower unit likely result from a long period of kinetic ice crystal growth indicating a minimum age of several hundred years. A radiocarbon date of 3 487 ± 20 cal BP suggests that ice patches could potentially date back to the late Holocene. This old ice was recently truncated during warmer summers between 2008 and 2012, but the ice patch quickly recovered its volume during cooler summers. The old age of the ice patches and their rapid regeneration after melt events suggest their resilience to current warmer summers.

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 score0.120
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.239
Teacher spread0.179 · 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

Citations7
Published2022
Admission routes4
Has abstractyes

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