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Record W4392920010 · doi:10.5376/ijms.2024.14.0004

The Potential Impact of Climate Change on the Diffusion of Plastic Waste in the Ocean

2024· article· en· W4392920010 on OpenAlexvenueno aff
Jinni Wu, Lei Chen

Bibliographic record

VenueInternational Journal of Marine Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changePlastic wasteEnvironmental scienceDiffusionOceanographyWaste managementGeologyEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

In recent years, global climate change and marine plastic pollution have become serious threats to the Earth's ecosystem and human society. Climate change has led to rising temperatures, rising sea levels, and an increase in extreme weather events, and the spread of plastic waste in the ocean has become a global problem, posing a threat to marine ecosystems and biodiversity. This study introduces the concept of climate change and focuses on exploring its direct impact on the marine environment, including factors such as rising seawater temperature, changes in ocean circulation, and acidification; discussed how climate change affects the distribution and decomposition of marine plastic waste. At the same time, this study delves into the potential impact of climate change on plastic waste in the ocean, from the negative impacts of climate change on marine ecosystems, including threats to biodiversity and disruptions to the food chain. Understanding this relationship is crucial for taking effective measures to reduce plastic pollution in the ocean. This not only provides a reliable basis for research on marine ecological environment, but also brings deeper insights into human health and sustainable development.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.208

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 communication0.0000.000
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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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

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