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Record W2558910118 · doi:10.1002/2016jc011890

Seasonal variation of the Beaufort shelfbreak jet and its relationship to Arctic cetacean occurrence

2016· article· en· W2558910118 on OpenAlexaff
Peigen Lin, Robert S. Pickart, Kathleen M. Stafford, G. W. K. Moore, Daniel J. Torres, Frank Bahr, Jianyu Hu

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUpwellingBeaufort scaleGeologyClimatologyOceanographyArcticMooringStormSeasonalityEnvironmental scienceAtmospheric sciences

Abstract

fetched live from OpenAlex

Using mooring time series from September 2008 to August 2012, together with ancillary atmospheric and satellite data sets, we quantify the seasonal variations of the shelfbreak jet in the Alaskan Beaufort Sea and explore connections to the occurrences of bowhead and beluga whales. Wind patterns during the 4 year study period are different from the long-term climatological conditions that the springtime peak in easterly winds shifted from May to June and the autumn peak was limited to October instead of extending farther into the fall. These changes were primarily due to the behavior of the two regional atmospheric centers of action, the Aleutian Low and Beaufort High. The volume transport of the shelfbreak jet, which peaks in the summer, was decomposed into a background (weak wind) component and a winddriven component. The wind-driven component is correlated to the Pt. Barrow, AK alongcoast wind speed record although a more accurate prediction is obtained when considering the ice thickness at the mooring site. An upwelling index reveals that wind-driven upwelling is enhanced in June and October when storms are stronger and longer-lasting. The seasonal variation of Arctic cetacean occurrence is dominated by the eastward migration in spring, dictated by pack-ice patterns, and westward migration in fall, coincident with the autumn peak in shelfbreak upwelling intensity.

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)0.002
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 score0.013
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.037
GPT teacher head0.293
Teacher spread0.256 · 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

Citations45
Published2016
Admission routes1
Has abstractyes

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