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Record W2141409375 · doi:10.1029/2008jc005009

Upwelling on the continental slope of the Alaskan Beaufort Sea: Storms, ice, and oceanographic response

2009· article· en· W2141409375 on OpenAlexaff
Robert S. Pickart, G. W. K. Moore, Daniel J. Torres, Paula Fratantoni, Roger A. Goldsmith, Jiayan Yang

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUpwellingGeologyOceanographyContinental shelfOrographyStormClimatologyWinter stormPrecipitationMeteorologyGeography

Abstract

fetched live from OpenAlex

The characteristics of Pacific‐born storms that cause upwelling along the Beaufort Sea continental slope, the oceanographic response, and the modulation of the response due to sea ice are investigated. In fall 2002 a mooring array located near 152°W measured 11 significant upwelling events that brought warm and salty Atlantic water to shallow depths. When comparing the storms that caused these events to other Aleutian lows that did not induce upwelling, interesting trends emerged. Upwelling occurred most frequently when storms were located in a region near the eastern end of the Aleutian Island Arc and Alaskan Peninsula. Not only were these storms deep but they generally had northward‐tending trajectories. While the steering flow aloft aided this northward progression, the occurrence of lee cyclogenesis due to the orography of Alaska seems to play a role as well in expanding the meridional influence of the storms. In late fall and early winter both the intensity and frequency of the upwelling diminished significantly at the array site. It is argued that the reduction in amplitude was due to the onset of heavy pack ice, while the decreased frequency was due to two different upper‐level atmospheric blocking patterns inhibiting the far field influence of the storms.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.083
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.264
Teacher spread0.245 · 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

Citations130
Published2009
Admission routes1
Has abstractyes

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