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Record W4403054804 · doi:10.1088/2515-7620/ad82b4

On the definition of the marginal ice zone: a case study with SAR and passive microwave data

2024· article· en· W4403054804 on OpenAlexafffund
Armina Soleymani, Muhammed Patel, Linlin Xu, K. Andrea Scott

Bibliographic record

VenueEnvironmental Research Communications · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Waterloo
FundersGlobal Water FuturesNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change Canada
KeywordsMicrowaveGeologyRemote sensingSynthetic aperture radarGeodesyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Abstract Widening and increasing extent of the marginal ice zone (MIZ), a transitional area between the open ocean and the pack ice, underscores the scientific significance of observing the MIZ. In the present study, we employed passive microwave (PM) and synthetic aperture radar (SAR) sea ice concentration (SIC) in the Greenland Sea and Beaufort Sea in November 2021 to detect the MIZ using two different MIZ definitions: SIC threshold-based (MIZ t ) and SIC anomaly-based (MIZ σ ). This study is the first to compare the SIC threshold-based with SIC anomaly-based MIZ definition using two different sources of SIC data. Our findings reveal that the SIC anomaly-based definition delineates a spatially extensive MIZ, capturing SIC variation attributed to sea ice growth. We also found that SAR data, compared to PM data, consistently identifies a broader MIZ region and is less sensitive to the threshold for the SIC anomaly standard deviation, underscoring the importance of selecting the appropriate MIZ definition.

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 score0.274
Threshold uncertainty score0.637

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 studies0.0010.001
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 head0.092
GPT teacher head0.312
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.

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

Citations3
Published2024
Admission routes2
Has abstractyes

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