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Record W1989670828 · doi:10.1080/07055900.2001.9649677

An analysis of the dissolution of ice in Nares Strait using AVHRR Imagery

2001· article· en· W1989670828 on OpenAlexafffundvenueabout
Ron Vincent, R. F. Marsden

Bibliographic record

VenueATMOSPHERE-OCEAN · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsRoyal Military College of Canada
FundersNational Oceanic and Atmospheric AdministrationMinistère de la Défense Nationale
KeywordsGeologyRemote sensingDissolutionPhysical geographyGeographyEngineering

Abstract

fetched live from OpenAlex

Abstract The North Water Polynya is the largest polynya in the Canadian Arctic. Its northern boundary is defined by a blockage, or ice bridge, that spans Smith Sound. The maintenance of the ice bridge, and the polynya itself, is contingent upon the southward flow of ice from the Lincoln Sea through Nares Strait. This paper analyzes the dissolution of ice in Nares Strait using Advanced Very High Resolution Radiometer (AVHRR) data. From March to August 1998, 1440 images were downloaded by a satellite receiver at Canadian Forces Station (CFS) Alert on the northern tip of Ellesmere Island. A preliminary evaluation of the data included a visual assessment of over 300 cloud‐free images. Of most interest were 42 images that revealed a rapid dissolution of ice in Nares Strait during mid‐June. In a four‐day span the area of open water in Nares Strait increased from 400 km2 to 1200 km2. Subsequent analysis of selected scenes included the application of both a sea and ice surface temperature algorithm. Based on the satellite imagery and archived weather data from CFS Alert, the ice in Nares Strait initially began to weaken and break‐up due to in situ melting. The significant reconfiguration of ice observed during mid‐June was the result of high winds funnelling southward through the channel.

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 score0.193
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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

Citations13
Published2001
Admission routes4
Has abstractyes

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