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Record W3088524114 · doi:10.1139/as-2020-0024

Sea ice and snow phenology in the Canadian Arctic Archipelago from 1997 to 2018

2020· article· en· W3088524114 on OpenAlexafffundvenueabout
Alicia L.A. Dauginis, Laura C. Brown

Bibliographic record

VenueArctic Science · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto MississaugaEnvironment and Climate Change CanadaUniversity of Toronto
KeywordsSnowSea iceArctic ice packArcticClimatologyArchipelagoCryosphereSnow fieldAntarctic sea icePhysical geographySnow lineEnvironmental scienceOceanographyGeologySnow coverGeographyGeomorphology

Abstract

fetched live from OpenAlex

The multiple islands and narrow channels that form the Canadian Arctic Archipelago (CAA) complicate snow/ice monitoring, as coarse resolution satellite observations are unable to resolve smaller-scale changes in snow/ice cover. We present the first study showing the utility of the Interactive Multisensor Snow and Ice Mapping System (IMS) 24 km (1997–2018) and 4 km (2004–2018) products to investigate changes in sea ice and snow phenology together in the CAA. Although ice break-up and snow retreat are shifting earlier (p > 0.05), on par with other Arctic regions, the final summer clearing of ice is shifting later. This, combined with trends towards earlier ice freeze and snow fall (p < 0.05), result in shorter open water and snow free seasons in the CAA. Spatial links between sea ice and snow are evident as significant clusters of trends were identified for all phenology parameters. The western regions were dominated by shifts towards shorter snow/ice seasons, whereas eastern regions tended to exhibit longer cover. Our research highlights the considerable regional and interannual variability in the timing of sea ice and snow advance/retreat within the CAA and emphasizes how the ice and snow dynamics in this complex region are responding to ongoing changing climate conditions.

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)0.001
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 score0.159
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.019
GPT teacher head0.214
Teacher spread0.195 · 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

Citations15
Published2020
Admission routes4
Has abstractyes

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