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Record W4407423426 · doi:10.3390/coasts5010007

A Marine Season Metric for Foxe Basin, Nunavut, Canada: Insights into the Evolving Nature of Sea-Ice Breakup and Freeze-Up

2025· article· en· W4407423426 on OpenAlexafffundabout
William A. Gough

Bibliographic record

VenueCoasts · 2025
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
KeywordsBreakupMetric (unit)Structural basinSea iceOceanographyGeographyGeologyPhysical geographyClimatologyPaleontologyEngineeringPsychology

Abstract

fetched live from OpenAlex

A new marine climate metric, marine season, is introduced for Foxe Basin, Nunavut, Canada capturing the time of the year that the Basin is influenced by open water. The metric is developed with a day-to-day temperature variability framework using the Hall Beach (Sanirajak) climate record (1957–2023). Day-to-day minimum temperature variability provides a clear signal of the marine season. The new metric is compared to the more traditional breakup and freeze-up dates of sea ice that uses a 5/10th sea-ice spatial coverage threshold. While the two metrics are in general agreement, some important differences occur related to the time required for the breakup (full ice coverage to 5/10th sea-ice coverage). The timing from onset of the marine season to 5/10th ice coverage has shortened in time in a statistically significant fashion, indicating a more rapid breakup in recent years. In contrast, the freeze-up period, 5/10th to full sea-ice coverage has increased. The longer ice-free season, as determined by sea-ice data, arises primarily from open water changes in the breakup (shorter) and freeze-up (longer) period timing. These are novel insights that suggest that the basic sea-ice regime, oscillating from a full sea-ice platform and ice-free conditions has not changed, but rather the observed changes are in the nature of the transitions between these two states, breakup and freeze-up.

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 score0.332
Threshold uncertainty score0.407

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 head0.004
GPT teacher head0.199
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

Citations0
Published2025
Admission routes3
Has abstractyes

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