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Record W2111943769 · doi:10.1080/01431160601105900

On the link between SAR‐derived sea ice melt and development of the summer upper ocean mixed layer in the North Open Water Polynya

2007· article· en· W2111943769 on OpenAlexafffund
R. J. Galley, David G. Barber, John Yackel

Bibliographic record

VenueInternational Journal of Remote Sensing · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of CalgaryUniversity of Manitoba
FundersArcticNetUniversity of Manitoba
KeywordsMixed layerSea iceSalinityStratification (seeds)GeologyOceanographyClimatologyTemperature salinity diagramsEnvironmental science

Abstract

fetched live from OpenAlex

We examine the ability of SAR‐derived landfast sea ice thermodynamic state surrounding the North Water Polynya to predict the timing of a shallow, stably stratified summer ocean mixed layer in the region. Radarsat‐1 ScanSAR is used to derive melt and pond onset dates over landfast sea ice surrounding the polynya, describing its thermodynamic evolution. Salinity and temperature profiles, connected in time and space, are complemented by potential density profiles and mixed layer depths, which describe the evolution of the upper ocean mixed layer between 4 April and 21 July 1998. Surface salinity driven potential density stratification and summer mixed layer depths are temporally coincident. Sea ice–ocean connection is first described at the local scale, then regionally. At the local scale, a reduction in surface salinity occurs between the melt and pond onsets over landfast sea ice. At the polynya scale, interpolated pond onset date is highly effective at predicting the timing of a thin, stably stratified summer ocean mixed layer; this has consequences for the potential prediction of timing of maximum ecosystem productivity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.151

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 head0.031
GPT teacher head0.260
Teacher spread0.228 · 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

Citations1
Published2007
Admission routes2
Has abstractyes

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