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Record W4284889740 · doi:10.1139/as-2021-0045

Development of a small-scale approach to assess sea ice change using weekly ice charts, with application to Anguniaqvia Niqiqyuam Marine Protected Area

2022· article· en· W4284889740 on OpenAlexafffundvenue
Kevin J. Gully, John Iacozza, Karen M. Dunmall

Bibliographic record

VenueArctic Science · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of ManitobaFisheries and Oceans Canada
FundersFisheries and Oceans CanadaFisheries Joint Management CommitteeUniversity of Manitoba
KeywordsSea iceOceanographyArctic ice packEnvironmental scienceClimate changeArcticPhysical geographyClimatologyGeographyGeology

Abstract

fetched live from OpenAlex

Broad-scale changes in sea ice have been documented across the Arctic; however, less is known about sea ice decline at smaller scales, focused at high-priority areas such as marine protected areas (MPAs) or places identified as important by Indigenous Peoples. Here, we develop a small-scale application of assessing sea ice change using weekly sea ice charts and apply that to assess sea ice change in Anguniaqvia Niqiqyuam MPA (ANMPA) from 1980 to 2019. Over that 40-year period, sea ice coverage in ANMPA decreased and open water increased by approximately a month (31.6 days at 50% ice; 33.8 days at 20% ice remaining during break-up and 80% ice formed during freeze-up). Break-up has gone from occurring in mid- or late July to occurring in late June or early July. Freeze-up has changed from occurring in mid-October to occurring in early November. As sea ice decline may have dramatic impacts for the ecosystem and consequences for the people that rely on this important area, we highlight the need to better understand the impacts of sea ice decline in small-scale priority places and also contribute to the development of community-scale approaches to increase the accessibility of assessing change.

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 score0.214
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
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 head0.052
GPT teacher head0.235
Teacher spread0.183 · 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
Published2022
Admission routes3
Has abstractyes

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