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Record W2997466382 · doi:10.1038/s41598-019-56780-6

A Study of the North Water Polynya Ice Arch using Four Decades of Satellite Data

2019· article· en· W2997466382 on OpenAlexafffund
Ron Vincent

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsRoyal Military College of Canada
FundersNational Oceanic and Atmospheric AdministrationGovernment of Canada
KeywordsSea iceArcticClimatologyArctic ice packGeologySatelliteFast iceAntarctic sea iceDrift iceOceanographySatellite imagery

Abstract

fetched live from OpenAlex

Polynyas are sections of the polar ocean that remain relatively ice-free during winter, imparting significant physical and biological impact on the region. The North Water polynya (NOW) situated between Ellesmere Island and Greenland is the largest recurring Arctic polynya. Historically, the NOW forms every season when Arctic Ocean floes moving southward through Nares Strait become congested and form an ice arch that defines the northern border of the polynya. This blockage usually forms during winter and breaks down in spring. It is conjectured that the polynya is maintained by latent heat of fusion from the continuous formation of new ice as floes are swept southward from the ice arch by wind and ocean currents. Analysis of four decades of satellite imagery indicates a growing instability in the location of the ice arch, challenging previous models of polynya maintenance. A linear trend of the data indicates the number of days of Nares Strait blockage has decreased 2.1 days/year between 1979 and 2019 with wide interannual variations. Prior to 2007, ice arches blocked Nares Strait an average of 177 days/year compared to 128 days/year since that time. The overall trend of reduced ice arch duration is a contributing factor to the dramatic loss of multiyear ice in the Arctic basin.

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 score0.011
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.047
GPT teacher head0.253
Teacher spread0.205 · 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

Citations67
Published2019
Admission routes2
Has abstractyes

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