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Record W2924568070 · doi:10.1029/2019gl082344

Revisiting the Circulation of Hudson Bay: Evidence for a Seasonal Pattern

2019· article· en· W2924568070 on OpenAlexafffundabout
Natasha A. Ridenour, Xianmin Hu, Kevin Sydor, Paul G. Myers, David G. Barber

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of ManitobaManitoba HydroUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaWestern Canada Research GridManitoba HydroCompute Canada
KeywordsBayGeostrophic windAnticycloneClimatologyOceanographyCirculation (fluid dynamics)Forcing (mathematics)GeologyCurrent (fluid)Ocean currentBoundary currentSurface runoffEnvironmental science

Abstract

fetched live from OpenAlex

Abstract The Hudson Bay Complex is the outlet for many Canadian rivers, receiving roughly 900 km3/year of river runoff. Historically, studies found a consistent cyclonic flow year‐round in Hudson Bay, due to the geostrophic boundary current induced by river discharge and cyclonic wind forcing that was supported by available observations at that time. Using a high‐resolution ocean general circulation model, we show that in summer, the mean circulation is not cyclonic but consists of multiple small cyclonic and anticyclonic features, with the mean flow directed through the center of the bay. Absolute Dynamic Topography and velocity observations also show this seasonal flow pattern. We find that this summer circulation is driven by geostrophic currents, generated by steric height gradients, which are induced by increased river discharge during the spring freshet, and reinforced by anticyclonic seasonal wind patterns.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.854

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.059
GPT teacher head0.320
Teacher spread0.261 · 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 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

Citations40
Published2019
Admission routes3
Has abstractyes

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