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Record W4401648639 · doi:10.5376/ijms.2024.14.0026

Variability of Indo-Pacific Ocean Basin Circulation and Its Impact on Climate Change

2024· article· en· W4401648639 on OpenAlexvenueno aff

Bibliographic record

VenueInternational Journal of Marine Science · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsIndo-PacificCirculation (fluid dynamics)OceanographyClimatologyClimate changeStructural basinEnvironmental scienceGeographyGeologyFisheryBiologyEngineering

Abstract

fetched live from OpenAlex

This study aims to understand the complex interactions between oceanic and atmospheric processes in the Indo-Pacific region and how these interactions influence global climate patterns. The study reveals several key findings. The Indo-Pacific region exhibits significant variability in ocean circulation patterns, which are influenced by phenomena such as the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The Indonesian Throughflow (ITF) plays a crucial role in modulating these patterns, with its variability linked to both local and inter-basin processes. Additionally, the study highlights the impact of basin-wide warming in the Indian Ocean on regional climate, particularly the Asian summer monsoon. The study also underscores the importance of understanding multi-decadal variability and its interaction with anthropogenic climate change. The findings of this study have significant implications for climate prediction and risk management. The variability of the Indo-Pacific Ocean basin circulation is a critical factor in global climate dynamics, influencing weather patterns, monsoon systems, and long-term climate trends. Improved understanding of these processes is essential for enhancing climate models and developing more accurate seasonal and decadal climate predictions. This study contributes to the broader effort to mitigate the impacts of climate change by providing insights into the complex interactions within the Indo-Pacific region.

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)0.000
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 score0.151
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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