MétaCan
Menu
Back to cohort
Record W4401651460 · doi:10.5376/ijms.2024.14.0023

Evaluating the Mechanisms of Coastal Circulation and Their Responses to Climate Change

2024· article· en· W4401651460 on OpenAlexvenueno aff
L. Chen, Qiong Wang

Bibliographic record

VenueInternational Journal of Marine Science · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsCirculation (fluid dynamics)Climate changeClimatologyEnvironmental scienceOceanographyGeographyGeologyEngineering

Abstract

fetched live from OpenAlex

Coastal circulation is driven by factors such as wind, tides, and thermohaline gradients, and is significantly influenced by coastal topography, adding to its complexity. This study systematically evaluates the mechanisms of coastal circulation and their responses to climate change, emphasizing the crucial role of these processes in understanding ocean dynamics and predicting future changes. By examining case studies from various regions, the study discusses the regional variations in coastal circulation and the effectiveness of current models in predicting these changes. Innovations in technology and methodology, such as improvements in modeling techniques and advances in observational technologies, provide new perspectives and tools for researching coastal circulation. Integrating these detailed data into advanced numerical models helps to more accurately predict the performance of coastal circulation under the impacts of climate change, especially in terms of changes in wind patterns, sea level rise, and changes in ocean temperature and salinity. This study also identifies challenges and knowledge gaps in the research, and proposes future research directions to better prepare for and mitigate the impacts of climate change on these critical systems.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.054
GPT teacher head0.337
Teacher spread0.283 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Marine ScienceSame topicOcean Acidification Effects and ResponsesFrench-language works237,207