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Record W2101868834 · doi:10.1029/2006jc003807

Influence of the Icelandic Low latitude on the frequency of Greenland tip jet events: Implications for Irminger Sea convection

2007· article· en· W2101868834 on OpenAlexaboutno aff
Faez Bakalian, Sultan Hameed, Robert S. Pickart

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsGeologyOceanographyIcelandicJet (fluid)ClimatologyNorth Atlantic oscillationJet streamLatitudeGroenlandiaMiddle latitudesConvectionAtmospheric sciencesGeographyMeteorologyIce sheetGeodesyPhysics

Abstract

fetched live from OpenAlex

The occurrence of Greenland tip jet events has been reported as the dominant factor controlling the formation of intermediate water in the Irminger Sea. It has been suggested that the frequency of these events is correlated with the North Atlantic Oscillation. To examine this process in more detail, we separate the North Atlantic Oscillation into Icelandic Low and Azores High components and carry out a regression fit of the frequency of tip jet events between 1961 and 2005. Our findings suggest that the frequency of Greenland tip jet events is highly dependent on the latitude of the Icelandic Low and the 2‐year time‐lagged February Icelandic Low latitude, with R2 = 0.48. We find that the winds near the southern tip of Greenland are predominately westerly during years when the Iceland Low is located above 63°N latitude. These conditions also correspond to colder air temperatures in the Labrador and Irminger Seas, implying larger oceanic heat losses due to the Greenland tip jet events and hence stronger convective overturning in the Irminger Sea.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.335
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations39
Published2007
Admission routes1
Has abstractyes

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