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Record W4362557367 · doi:10.3390/coasts3020007

Temporal and Spatial Evolution of Seasonal Sea Ice of Arctic Bay, Nunavut

2023· article· en· W4362557367 on OpenAlexafffundabout
Sławomir Kowal, William A. Gough, Kenneth Butler

Bibliographic record

VenueCoasts · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSea iceBayArctic ice packClimatologyArcticCryosphereOceanographyNorth Atlantic oscillationIce-albedo feedbackArctic sea ice declineClimate changeSeasonalityEnvironmental sciencePhysical geographyDrift iceGeographyGeologyEcology

Abstract

fetched live from OpenAlex

The temporal and spatial variation in seasonal sea ice in Arctic Bay, Nunavut, are examined using time series and spatial clustering analyses. For the period of 1971 to 2018, a time series of sea ice break-up, and freeze-up, dates and ice-free season length at nine grid points are generated from sea ice charts derived from satellites and other data. These data are analysed temporally and spatially. The temporal analyses indicate an unambiguous response to a warming climate with statistically significant earlier break-up dates, later freeze-up dates, and longer ice-free seasons with clear statistically significant linkages to local air temperature. The rate of change in freeze-up dates and ice-free season length was particularly strong in the early 2000s and less in the 2010s. Spatial clustering analysis indicated a roughly linear pathway of south to north behaviour, following the contours of the bay with the exception of modified behaviour for landfast sea ice near the hamlet of Arctic Bay. The temporal analysis confirms and expands upon an earlier time series analysis of local seasonal sea ice. The spatial analysis indicates that while the ice-free season is increasing, it does not provide clear evidence that there has been a regime change in the seasonal characteristics of how sea ice forms and melts each year.

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 score0.089
Threshold uncertainty score0.998

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 studies0.0000.000
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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

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