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Record W4285130958 · doi:10.1525/elementa.2021.00073

Landfast sea ice in Hudson Bay and James Bay

2022· article· en· W4285130958 on OpenAlexaffabout
Kaushik Gupta, Anirban Mukhopadhyay, David G. Babb, David G. Barber, Jens K. Ehn

Bibliographic record

VenueElementa Science of the Anthropocene · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBaySea iceOceanographyArctic ice packGeologyArcticMelt pondIce shelfClimatologyAntarctic sea icePhysical geographyCryosphereGeography

Abstract

fetched live from OpenAlex

Through analysis of Canadian Ice Service ice charts, we have characterized the temporal and spatial variability of landfast sea ice (or fast ice) surrounding Hudson Bay and James Bay from 2000 to 2019. Over this 19-year period, we observed contrasting changes in fast-ice persistence between the western and eastern sides of Hudson Bay and James Bay. Fast ice in western Hudson Bay and James Bay trended towards later freeze-up and earlier break-up that resulted in a shortening of the fast-ice season at a rate of 6 days/decade. Contrastingly, eastern Hudson Bay and James Bay showcased relatively earlier freeze-up and delayed break-up, and an overall trend towards a longer fast-ice season at a rate of 8 days/decade. The general trend in air temperature followed a similar spatial pattern to the changing fast-ice persistence; however, the timing of fast-ice break-up did not have a strong relationship with the thawing-degree days during spring. Variations in fast-ice area showed latitudinal and meridional gradients, with greater fast-ice area in eastern Hudson Bay and James Bay compared to the west. Given the overall warming trend in the Arctic, observing areas of decreasing fast-ice persistence is unexpected; however, this study highlights the role of regional factors, such as coastal orientation and bathymetry, in controlling the stability, growth and decay of fast ice.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.998

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)0.0030.000

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 head0.008
GPT teacher head0.229
Teacher spread0.220 · 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.

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

Citations24
Published2022
Admission routes2
Has abstractyes

Explore more

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