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Record W2745029764 · doi:10.1175/jpo-d-17-0051.1

Seasonal Overturning of the Labrador Sea as Observed by Argo Floats

2017· article· en· W2745029764 on OpenAlexaboutno aff
James Holte

Bibliographic record

VenueJournal of Physical Oceanography · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsArgoHydrographyGeostrophic windOceanographyGeologyThermohaline circulationClimatology

Abstract

fetched live from OpenAlex

Abstract Argo floats are used to investigate Labrador Sea overturning and its variability on seasonal time scales. This is the first application of Argo floats to estimate overturning in a deep-water formation region in the North Atlantic. Unlike hydrographic measurements, which are typically confined to the summer season, floats offer the advantage of collecting data in all seasons. Seasonal composite potential density and absolute geostrophic velocity sections across the mouth of the Labrador Sea assembled from float profiles and trajectories at 1000 m are used to calculate the horizontal and overturning circulations. The overturning exhibits a pronounced seasonal cycle; in depth space the overturning doubles throughout the course of the year, and in density space it triples. The largest overturning [1.2 Sv (1 Sv ≡ 10 6 m 3 s −1 ) in depth space and 3.9 Sv in density space] occurs in spring and corresponds to the outflow of recently formed Labrador Sea Water. The overturning decreases through summer and reaches a minimum in winter (0.6 Sv in depth space and 1.2 Sv in density space). The robustness of the Argo seasonal overturning is supported by a comparison to an overturning estimate based on hydrographic data from the AR7W line.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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

Citations41
Published2017
Admission routes1
Has abstractyes

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