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Temperature-based Diagnosis of the Gulf Stream Path Overestimates a Northward Shift in a Warming Ocean

2025· preprint· en· W4406722401 on OpenAlexaff
Lina Garcia Suarez, Katja Fennel, Neha Mehendale, Tronje P. Kemena, David P. Keller

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsClimatologyGlobal warmingEnvironmental scienceOceanographyGulf StreamPath (computing)Climate changeSea surface temperatureGeologyComputer science

Abstract

fetched live from OpenAlex

Changes in the Gulf Stream path are often seen as climate change indicators, with recent shifts driving rapid ecosystem alterations in the northwest Atlantic Ocean. Identifying the velocity axis of the Gulf Stream east of 70{degree sign}W is challenging due to meandering and eddy activity. This study uses high-resolution climate models to show that temperature-based criteria, especially the North Wall criterion, overestimate the northward shift of the Gulf Stream under high-emission scenarios by a factor of two to three. In contrast, a sea surface height (SSH)-based criterion remains more closely aligned with the true path, making it a more reliable tool for path estimations. Our analysis of velocity and temperature isotherms suggests rising seawater temperatures hinder accurate estimates of the Gulf Stream via temperature criteria alone. While temperature-based proxies are used in paleoclimate studies, our findings caution against their use for inferring changes in the Gulf Stream and AMOC.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.211
Teacher spread0.203 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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