MétaCan
Menu
Back to cohort

Global Analysis of Marine Heatwave physical Processes

2020· article· en· W3084480149 on OpenAlexaff
Sofia Darmaraki, Eric C. J. Oliver

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsDalhousie University
Fundersnot available
KeywordsClimate changeOceanographyMarine ecosystemEnvironmental scienceEcosystemSea surface temperatureGlobal warmingAdvectionClimatologyEcologyBiologyGeologyPhysics

Abstract

fetched live from OpenAlex

<p>Marine heatwaves (MHWs) are periods of extreme warm temperatures in the ocean and have been seen to exert substantial pressure to marine ecosystems around the world. For instance, they may drive widespread marine species die-offs, force coastal marine ecosystem regime shifts, promote toxic algal blooms, and/or alter the distribution of commercial fisheries on a scale of weeks to months. Recent studies have indicated a significant increase in MHW frequency and intensity throughout the 20<sup>th</sup> century, a trend which is likely to aggravate in the 21<sup>st</sup> century, according to future projections.  Therefore, it is crucial to understand what are the climate drivers and physical processes governing MHWs in different regions of the global ocean and how these may change under the climate change regime. Here, we perform a mixed layer heat budget analysis, using a global ocean reanalysis product, to diagnose the relative role of ocean advection and atmosphere fluxes on the development of past MHWs around the world. Significant events are first identified using a consistent framework. Then, the heat budget results reveal that certain physical processes tend to be dominant in different regions, which can be traced back to specific local-scale dynamics. The global scale of this analysis provides a significant addition to the current literature which has, so far, been focused on the examination of the underlying mechanisms behind individual events. It also contributes to a better understanding of the variability and processes governing MHWs, offering also a potential ability for future event predictability.</p>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.236
Teacher spread0.220 · 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.

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicOcean Acidification Effects and ResponsesFrench-language works237,207